image classification research papers


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Depth map generation by image classification
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ABSTRACT This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as

The use of census data in urban image classification
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Abstract A supervised classification strategy containing a suite of techniques that allow the linking of urban land cover from remotely sensed data with urban functional characteristics from population census data is outlined and demonstrated. For a stronger link, census

Performance evaluation of the nearest feature line method in image classification and retrieval
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IMAGE retrieval finds similar images in the ascending order of similarity or distance, while image classification classifies a query image into the predefined classes associated with the top-matched image. Both requires a definition of metric to measure the similarity in terms

Flexible, high performance convolutional neural networks for image classification
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Abstract We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the

Impacts of patch size and land-cover heterogeneity on thematic image classificationaccuracy
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Abstract Landscape chamcteristics such as small patch size and landcover heterogeneity have been hypothesized to increase the likelihood of mis-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support

What size window for image classification A cognitive perspective
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Abstract Windows are commonly used in digital image classification studies to define the local information content around a single pixel using a per-pixel classifier. Other studies use windows for characterizing the information content of a region, or group of pixels, in an

Support vector machines for remote sensing image classification
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ABSTRACT In the last decade, the application of statistical and neural network classifiers to remote-sensing images has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from

Image classification based on fuzzy logic
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ABSTRACT Fuzzy logic is relatively young theory. Major advantage of this theory is that it allows the natural description, in linguistic terms, of problems that should be solved rather than in terms of relationships between precise numerical values. This advantage, dealing

Hierarchical matching pursuit for image classification: Architecture and fast algorithms
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Abstract Extracting good representations from images is essential for many computer vision tasks. In this paper, we propose hierarchical matching pursuit (HMP), which builds a feature hierarchy layer-by-layer using an efficient matching pursuit encoder. It includes three

Application of DEM data to Landsat image classification: evaluation in a tropical wet-dry landscape of Thailand
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Abstract Integration of ancillary data in digital image classification has been shown to improve land-use/land-cover discrimination and classification accuracy. Studies demonstrating such techniques in the context of the tropical landscape are lacking. Cloud

Classification strategies for image classification in genetic programming
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Abstract This paper describes an approach to the use of genetic programming for multi-class image recognition problems. In this approach, the terminal set is constructed with image pixel statistics, the function set consists of arithmetic and conditional operators, and the

Principal component analysis for hyperspectral image classification
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ABSTRACT The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a

An Appraisal of a Decision Tree approach to Image Classification.
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ABSTRACT This paper investigates the applicability to a shape-recognition problem of a concept learning algorithm which generates decision rules from examples. A comprehensive analysis of this algorithm applied to an industrial vision problem is

Heterogeneous Transfer Learning for Image Classification.
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Abstract Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from

Multilevel Feature Extraction and X-ray Image Classication
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ABSTRACT The need of content based image retrieval tools increases with the enormous growth of digital medical image database. Classification of images is an important step of content based image retrieval (CBIR). In this study, we propose a new image

A Knowledge Engineering Approach for Image Classification based on Probabilistic Reasoning Systems.
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ABSTRACT We present a knowledge engineering approach for image classification that is based on probabilistic reasoning systems. The approach gives the knowledge engineer a systematic way to integrate multiple probabilistic classifiers. A case study of applying this

Object oriented analysis and semantic network for high resolution image classification
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ABSTRACT This work presents a high resolution image classification based on object oriented. The objects are derived by means of multiresolution segmentation. It allows a creation of different levels of segments supporting a hierarchy structure, generating spatial relations

Multispectral satellite image and ancillary data integration for geological classification
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Abstract Digital classification of Landsat imagery for geological purposes often gives poor results. To improve classification accuracy spectral data have been combined with ancillary data. These data have been used in pre-classification processing to enhance image

Land use classification of remote sensing image with GIS data based on spatial data mining techniques
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(School of Information Engineering, Wuhan Technical University of Surveying and mapping, No. 129 Luoyu Road, Wuhan, PR China, 430079)(* Institute of China Electronic System Engineering, No. 6, Wanshou Road, Beijing, PR China, 100036) Email: dli dns. wtusm.

Matrix completion for multi-label image classification
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Abstract Recently, image categorization has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. This paper formulates image categorization as a multi-label classification problem using recent advances in

Study of remote sensing image fusion and its application in image classification
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ABSTRACT Data fusion is a formal framework in which is expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of 'greater quality'will depend upon the application.

Generalized composite kernel framework for hyperspectral image classification
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ABSTRACT This paper presents a new framework for the de-velopment of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the

A system for region image classification based on textural measures
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Abstract. This work presents a system for region classification using textural measures. The user can extract and analyze any kind of textural measures provide by this system and thus classify a group of region samples based on a set of selected measures. The system was

A novel fuzzy weighted c-means method for image classification
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Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy

Multiview vector-valued manifold regularization for multilabel image classification
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ABSTRACT In computer vision, image datasets used for clas-sification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (eg, pedestrian, bicycle, and tree) and is properly characterized by The most widely used method for extracting surface information from remotely sensed images is image classification. With this technique, each pixel is assigned to one out of several known categories or classes through a separation approach. Thus an image is

Rough wavelet hybrid image classification scheme
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Abstract This paper introduces a new computer-aided classification system for detection of prostate cancer in Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification process, an intensity adjustment process is applied first,

Wavelet based multi class image classification using neural network
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ABSTRACT This paper presents feature extraction and classification of multiclass images by using Haar wavelet transform and back propagation neural network. The wavelet features are extracted from original texture images and corresponding complementary images. The This paper presents an application study of exploiting fuzzy-rough feature selection (FRFS) techniques in aid of efficient and accurate Mars terrain image classification. The employment of FRFS allows the induction of low-dimensionality feature sets from sample descriptions

Advances in object-based image classification
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ABSTRACT This paper presents a review of object-based image classification, outlining recent developments, assessing current capabilities and signposting future implications. Object-based classification methods are described, outlining advantages over other forms Abstract. The eighth edition of the ImageCLEF medical retrieval task was organized in 2011. A subset of the open access collection of PubMed Central was used as the database in 2011. This database contains 231,000 images and is substantially larger than previously The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two

Use of hidden Markov models and phenology for multitemporal satellite image classification: Applications to mountain vegetation classification
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ABSTRACT Ground cover classification based on a single satel-lite image can be challenging. The work reported here concerns the use of multitemporal satellite image data in order to alleviate this problem. We consider the problem of vegetation mapping and model the

Unsupervised image classification using the entropy/alpha/anisotropy method in radar polarimetry
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Abstract In this paper we re-examine the entropy alpha approach to radar polarimetry and show how the basic method may be augmented by the addition of two new polarizing parameters, the propagation and helicity phase angles and three depolarizing parameters

Multi-temporal Landsat image classification and change analysis of land cover in the Twin Cities (Minnesota) metropolitan area
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The growth of the size of cities, often at rates exceeding the population growth rate, and the accompanying loss of agricultural lands, forests and wetlands, escalating infrastructure costs, increases in traffic congestion, and degraded environments, is of growing concern

Morphological color size distributions for image classification and retrieval
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ABSTRACT Current content-based image retrieval techniques can typically perform efficient and effective searches on heterogeneous image databases. This contribution deals with an approach based on the integration of color and texture description which is applied to a

Semi-supervised self-learning for hyperspectral image classification
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ABSTRACT Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high- dimensional images with hundreds of spectral bands. Supervised hyperspectral image

Winter road condition recognition using video image classification
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Sweden spends 1.7 billion Crowns on winter road maintenance annually. A large part of this money goes into plowing, salting, and sanding of the roads. The decision about what maintenance to perform is made, in part, based on data received from road weather

Random Forests: An algorithm for image classification and generation of continuous fields data sets
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ABSTRACT Random forests is a classification and regression algorithm originally designed for the machine learning community. This algorithm is increasingly being applied to satellite and aerial image classification and the creation of continuous fields data sets, such as,

A simulation comparison of three marginal area estimators for image classification
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Abstract Obtaining correct area estimations for different land-cover or environmental categories is one of the main objectives of environmental applications of statistics. Area estimations are often obtained through classifying surveyed or remotely sensed data

Comparison studies on classification for remote sensing image based on data mining method
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ABSTRACT Data mining methods have been widely applied on the area of remote sensing classification in recent years. In these methods, neural network, rough sets and support vector machine (SVM) have received more and more attentions. Although all of them have

Satellite image classification using expert structural knowledge: a method based on fuzzy partition computation and simulated annealing
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Abstract The design of automatic systems dedicated to satellite image classification has received considerable attention. However, the current systems still cannot compare with human photo-interpreters. A promising approach consists in integrating structural

Comparing Different Satellite Image Classification Methods: An Application In Ayvalk District, Western Turkey
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ABSTRACT The different satellite image classification methods were compared using the satellite images of the Ayvalik district located on the western coast of Turkey covering approximately 560 km2. For this purpose, landuse classification of the investigation area

Text-based hierarchical image classification and retrieval of stock photography
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Abstract Tony Stone Images have designed a textual classification structure and an image retrieval system to store and retrieve pictures within the stock photography domain. The image retrieval system has been designed from the stock photography domain-specifics

Classification and mining of brain image data using adaptive recursive partitioning methods: application to Alzheimer disease and brain activation patterns
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Purpose: To effectively identify discriminative spatial areas in MRI and fMRI and make image classification, similarity searches and mining of associations between spatial distributions and other clinical assessment feasible we have developed brain informatics tools that are The contextual analysis of a multitemporal sequence of images of a given site represents a way to improve the accuracy with respect to the non-contextual single-time classification. The proposed contextual multitemporal classification scheme consists of two stages of

Hyperspectral image classification with mahalanobis relevance vector machines.
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Machines (RVM) for remote sensing hyperspectral image classification. We also include the Mahalanobis kernel in the formulation of the RVM to take into account the covariance of the features in the classification process. Experimental results in different scenarios confirm

A New Approach in Image Classification
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ABSTRACT A simply implemented algorithm for analysis and recognition of images is presented. The suggested method is based on Toeplitz matrices and their determinants. The algorithm classifies the image characteristic points into a feature vector whose elements

Image Classification using Data Compression Based Techniques
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Earth Observation applications are seldom usable on different kinds of data types, being strongly dependant on the characteristics of the sensor used (ie spatial, spectral and radiometric resolutions of the data), models adopted and a priori assumptions. We

Max-margin Latent Dirichlet Allocation for Image Classification and Annotation.
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Abstract We present the max-margin latent Dirichlet allocation, a max-margin variant of supervised topic models, for image classification and annotation. Our model for image classification (called MMLDAc) integrates discriminative classification with generative To monitor, analyze and interpret developments in our changing environment, spatial data are periodically collected and processed. Remote sensing is a valuable source for this purpose. Probabilistic methods can be used to extract thematic information from spectral

Mars terrain image classification using cartesian genetic programming
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ABSTRACT Automatically classifying terrain such as rocks, sand and gravel from images is a challenging machine vision problem. In addition to human designed approaches, a great deal of progress has been made using machine learning techniques to perform Texture classification is a trendy and a catchy technology in the field of texture analysis. It finds its applications in various fields like medical image classification, computer vision, remote sensing, machine vision, agricultural field and many more. Our main focus is on

Methods for automatic extraction of regularity patterns and its application to object-orientedimage classification
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ABSTRACT Detection and quantification of regularity patterns are important structural aspects for object-oriented classification of images for geo-databases updating. Four image processing methods are analysed and evaluated for this purpose: semivariogram analysis

Hyperspectral image classification using a self-organizing map
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The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging

Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloiniki, Greece
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Abstract This paper describes the methodology and results of classifications of multi- temporal Landsat TM/ETM+ data of the Prefecture of Thessaloniki, Macedonia Greece for

Modeling and estimation of spatial random trees with application to image classification.
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ABSTRACT A new class of multiscale multidimensional stochastic processes called spatial random trees is introduced. The model is based on multiscale stochastic trees with stochastic structure as well as stochastic states. Procedures are developed for exact

Scene image classification with biased spatial block and pLSA
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Abstract Scene image classification is a fundamental problem in the fields of computer vision and image understanding. A novel scene image classification method based on biased spatial block information and an improved coding approach in bag-of-visual-words (BOW)

A user friendly statistical system for polarimetric SAR image classification
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ABSTRACT This article presents a system for polarimetric SAR image classification. This system uses contextual information through a Markovian model for the classes, besides a statistical model for the data. It is developed with the user in mind and, therefore, it is

Wavelet Use for Image Classification
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ABSTRACT The paper presents selected mathematical methods of image analysis including their segmentation, thresholding and feature extraction to detect specific image regions and to find their properties. The main part of the paper presents possibilities of the application

A Nectar of Frequent Approximate Subgraph Mining for Image Classification Un nectar sobre la minera de subgrafos frecuentes aproximados en clasificacin
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Resumen La minera de subgrafos frecuentes aproximados ha emergido como un importante tpico de investigacin donde los grafos son usados para modelar entidades y

Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering.
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ABSTRACT In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and

Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting
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ABSTRACT In this paper, we propose new reversible water-marking scheme. One first contribution is a histogram shifting modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction-errors and by

A Semi-Supervised Feature Extraction based on Supervised and Fuzzy-based Linear Discriminant Analysis for Hyperspectral Image Classification
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ABSTRACT Linear discriminant analysis (LDA) is a commonly used feature extraction method to resolve the Hughes phenomenon for classification. Moreover, many studies show that the spatial information can greatly improve the classification performance. Hence, for

ANALYSIS SPARSE CODING MODELS FOR IMAGE-BASED CLASSIFICATION
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ABSTRACT Data-driven sparse models have been shown to give superior performance for image classification tasks. Most of these works depend on learning a synthesis dictionary and the corresponding sparse code for recognition. However in recent years, an alternate

A REVIEW ON NEW DATA MINING TECHNIQUES FOR X-RAY IMAGE CLASSIFICATION
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ABSTRACT With the rapid development of the medical science more and more medical images are generated rapidly like MRI, CT scan, X-ray etc. Due to that an efficient system is essential for the indexing, storing and analyzing such images. The analyzing cost of such

A Survey of Image Classification Methods and Techniques
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ABSTRACT In this paper, we review the current activity of image classification methodologies and techniques. Image classification is a complex process which depends upon various factors. Here, we discuss about the current techniques, problems as well as prospects of

Multi-class Image Classification Based on Fast Stochastic Gradient Boosting
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Lin Li1, 2, Yue Wu1 and Mao Ye1 1School. of Computer Science and Engineering, University of Electronic Science and Technology of China No. 2006, Xiyuan Ave, West Hi- Tech Zone, Chengdu, Chinalilin200909 gmail. com 2Sichuan TOP IT

Polarimetric SAR Image Classification on Urban Area using a Subset Selection Method
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Abstract. In this study, we consider the capability of a single look high-resolution PoLSAR image for discriminating different surfaces in urban area. First, a basic framework is set up to extract different polarimetric descriptors from Sinclair matrix and coherency matrices,

Nonlinear dimensionality reduction via the ENH-LTSA method for hyperspectral image classification
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ABSTRACT The problems of neglecting spatial features in hyper-spectral imagery (HSI) and the high complexity of Local Tangent Space Alignment (LTSA) still exist in the nonlinear dimensionality reduction with LTSA for classification. Therefore, this paper proposes an

Image classification in natural scenes: Are a few selective spectral channels sufficient
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ABSTRACT A tenet of object classification is that accuracy improves with an increasing number (and variety) of spectral channels available to the classifier. Hyperspectral images provide hundreds of narrowband measurements over a wide spectral range, and offer

Optimizing matrix mapping with data dependent kernel for image classification
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Abstract. Kernel based nonlinear feature extraction is feasible to extract the feature of image for classification. The current kernel-based method endures two problems: 1) kernelbased method is to use the data vector through transforming the image matrix into vector, which

Analysis and exploitation of multipath ghosts in radar target image classification.
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ABSTRACT An analysis of the relationship between multipath ghosts and the direct target image for radar imaging is presented. A multipath point spread function (PSF) is defined that allows for specular reflections in the local environment and can allow the ghost images to

Gabor-filtering based nearest regularized subspace for hyperspectral image classification
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ABSTRACT By coupling the nearest-subspace classification with a distance-weighted Tikhonov regularization, nearest regularized subspace (NRS) was recently developed for hyperspectral image classification. However, the NRS was originally designed to be a

Crop Type Classification Based on Clonal Selection Algorithm for High Resolution SatelliteImage
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ABSTRACT This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a

Classification of Image at different Resolution using Rotation Invariant Model
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ABSTRACT In this paper a multi resolution-rotation invariant simultaneous autoregressive (MR- RISAR) model for texture classification along with a multivariate rotation-invariant SAR (RISAR) model which is based on the circular autoregressive (CAR) model have been

Multi-Task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
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(a) (b) 0 100 200 300 400 500 600 700 8004-20246810121416 (c) (d) 0 20 40 60 80 0 10 20 30 40 50 60 70 80 90 100 Recognition rate (%) SRC PCA + NN ICA I + NN LNMF + NN L2 + NS Fig. 11. Recognition under random corruption. Left: (a) Test images y from

Nearest Clustering Algorithm for Satellite Image Classification in Remote Sensing Applications.
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ABSTRACT Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum

Generalized Regular Spatial Pooling for Image Classification
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Abstract This paper discusses spatial pooling, a basic and crucial problem in the Bag-of- Features (BoF) model. Conventional algorithms such as Spatial Pyramid Matching (SPM)[1] hierarchically divide the image into exclusive and regular regions for feature

Active learning in social context for image classification
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ABSTRACT Motivated by the widespread adoption of social networks and the abundant availability of user-generated multimedia content, our purpose in this work is to investigate how the known principles of active learning for image classification fit in this newly

Advance Image Classification System.
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ABSTRACT Advance image classification system focuses on synthetic (eg non-photographic) Natural (eg photographic) images. The classification of images based on semantic description is a challenging and important problem in automatic image identification. An

Image Classification by Transfer Learning Based on the Predictive Ability of Each Attribute
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ABSTRACT Machine learning is the basis of important advances in artificial intelligence such as image and speech recognition and natural language processing. Unlike general machine learning, which uses the same task for training and testing, transfer learning uses the

Weed Image Classification using Wavelet Transform, Stepwise Linear Discriminant Analysis, and Support Vector Machines for an Automatic Spray Control
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We tested and validated the accuracy of wavelet transform along with stepwise linear discriminant analysis (SWLDA) and support vector machines (SVMs) for crop/weed classification for real time selective herbicides systems. Unlike previous systems, the

Contextual Remote Sensing Image Classification through Support Vector Machines, Markov Random Fields and Graph Cuts
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ABSTRACT The problem of remote-sensing image classification is addressed in this paper by proposing a novel contextual classification method that integrates support vector machines (SVMs), Markov random fields, and graph cuts. The proposed approach is

Adaptive knowledge transfer for multiple instance learning in image classification
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Abstract Multiple Instance Learning (MIL) is a popular learning technique in various vision tasks including image classification. However, most existing MIL methods do not consider the problem of insufficient examples in the given target category. In this case, it is difficult

Texture Based Hyperspectral Image Classification
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Page 1. www.iitk.ac.in brajeshk iitk.ac.in Texture Based Hyperspectral Image Classification By Brajesh Kumar and Prof. Onkar Dikshit Geo-Informatics Group Department of Civil Engineering

Column Transform based Feature Generation for Classification of Image Database
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ABSTRACT Designing computer programs to automatically classify images using low level or high level features is a challenging task in image processing. This paper proposes an efficient classification technique which is based on image transforms and nearest

Feature coding for image classification based on saliency detection and fuzzy reasoning and its application in elevator videos
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ABSTRACT Feature coding is an fundamental step in bag-of-words based model for image classification and have drawn increasing attention in recent works. However, there still exits ambiguity problem, and it is also sensitiveness to unusual features. To improve the The bag of words approach describes an image as a histogram of visual words. Therefore, the structural relation between words is lost. Since graphs are well adapted to represent these structural relations, we propose, in this paper, an image classification framework

Modified Semantic Classification for very large image database
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ABSTRACT Semantic indexing of images is an ongoing study. Recently many researchers in the field of image classification for indexing of the very large image databases are interested in object (s) in an image. Thus, for efficient image matching use the semantic gap between

Flickr Image Classification using SIFT Algorism
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ABSTRACT As a huge image storing and sharing site such as Flickr is getting popular, the amount of image information is also increasing and the users want more accurate image searching system. To increase the accuracy of tag-based image search, a variety of

Application Research on Cluster Analysis in Unsupervised Image Classification
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In order to improve the application effectiveness of cluster analysis in unsupervised image classification, a clustering algorithm based on density and adaptive density-reachable is designed and implemented. Compared with the classifying results of K-means and

CS365: Image Classification Using Self-taught Learning For Feature Discovery
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Abstract Image classification is an important task in computer vision which aims at classifying images based on their content. Most techniques for this task require a lot of labeled data to train the model which is scarce and expensive. Self-taught learning

Research on Image Classification Model of Probability Fusion Spectrum-Spatial Characteristics Based on Support Vector Machine
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Abstract For insufficient information of imaging spectrum with high spatial resolution, detailed imaging information, reduction of mixed pixels, increase of pure pixels and problems of image characteristic extraction and model classification produced from this,

Medical Image Classication Using Multi Vocabulary
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ABSTRACT In this study, the bag-of-visual-word based medical image classification technique was investigated. A new approach for medical image classification was proposed by introducing multi steps image classification using three different visual vocabularies.

A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information
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Abstract This paper reviewed major remote sensing image classification techniques, including pixel-wise, sub-pixel-wise, and object-based image classification methods, and highlighted the importance of incorporating spatio-contextual information in remote

Multi-label image classification with a probabilistic label enhancement model
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Abstract In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and

Multiple Classifier Ensembles with Band Clustering for Hyperspectral Image Classification
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Abstract Due to the high dimensionality of a hyperspectral image, classification accuracy of a single classifier may be limited when the size of the training set is small. A divide-and- conquer approach has been proposed, where a classifier is applied to each group of

FUNDAMENTALS OF DIGITAL IMAGE PROCESSING AND BASIC CONCEPT OFCLASSIFICATION
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ABSTRACT We have to classify and analyze digital images for different study and purposes. Digital images are obtained from sources like camera, satellites, aircraft etc. Data obtained from satellites or aircraft ie, the space based and remote sensing data needs to be

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