image classification


Image classification refers to a process in computer vision that can classify an image according to its visual content. For example, an image classification algorithm may be designed to tell if an image contains a human figure or not. An algorithm such as SURF is used to generate features.

Principal component analysis for hyperspectral image classification
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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 preprocessing technique

A survey on image classification approaches and techniques
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Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object

Matrix completion for multi-label image classification
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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 matrix

Image classification based on fuzzy logic
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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 with the

Patch-based convolutional neural network for whole slide tissue image classification
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Abstract Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally

Hierarchical matching pursuit for image classification : Architecture and fast algorithms
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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

Scale and texture in digital image classification
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Classification errors using texture are most likely associated with class edges, but investigators oflen avoid edges when evaluating texture for classification . The large windows needed to produce a stable texture measure produce large edge effects. Small

The use of census data in urban image classification
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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 tract data

The effectiveness of data augmentation in image classification using deep learning
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In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification . Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input

Dynamic clustering using particle swarm optimization with application in unsupervised image classification
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A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification . The proposed approach automatically determines the optimum number of clusters and simultaneously

Classification strategies for image classification in genetic programming
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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 fitness

Impacts of patch size and land-cover heterogeneity on thematic image classification accuracy
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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 these

Image classification using support vector machine and artificial neural network
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Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM)

Comparing image classification methods: K-nearest-neighbor and support-vector-machines
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In order for a robot or a computer to perform tasks, it must recognize what it is looking at. Given an image a computer must be able to classify what the image represents. While this is a fairly simple task for humans, it is not an easy task for computers. Computers must go

What size window for image classification A cognitive perspective
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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 area

Automatic medical image classification and abnormality detection using k-nearest neighbour
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This research work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction

Image classification in remote sensing
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One of the most important functions of remote sensing data is the production of Land Use and Land Cover maps and thus can be managed through a process called image classification . This paper looks into the following components related to the image

Multilevel feature extraction and X-ray image classification
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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 classification method

Random Forests: An algorithm for image classification and generation of continuous fields data sets
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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, percent

A survey on artificial intelligence approaches for medical image classification
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In this paper, a survey has been made on the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification . Several state-of-the- art Artificial Intelligence (AI) techniques for automation of biomedical image classification are


COMMENT computer-science, image-processing



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