image classification techniques in deep learning


Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. … The recommended way to perform classification and multivariate analysis is through the Image Classification toolbar.

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

SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH.
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The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of

A deep learning approach for tumor tissue image classification
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The performance of automatic tumor tissue classification using traditional machine learning methods greatly depends on the choice of meaningful descriptive features derived from the tissue images. Identification of such features requires domain-specific expert knowledge and

A survey of deep learning methods and software for image classification and object detection
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Autoencoders, Restricted Boltzmann Machines, Convolutional Neural Networks During the past years deep learning approaches that are heavily based on neural networks have been of special interest. Each node of a network (ie artificial neuron) is associated

A comparison of conventional and deep learning methods of image classification on a database of chest radiographs
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Purpose Recently, due to a number of demonstrated top-ranked achievements the deep learning methods and convolutional neural networks (CNNs) are of high interest for medical image analysis community. The purpose of this study is to compare abilities of CNNs and

Deep Learning in Big Image Data: Histology image classification for breast cancer diagnosis
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This paper present results of the use of Deep Learning approach and Convolutional Neural Networks (CNN) for the problem of breast cancer diagnosis. Specifically, the main goal of this particular study was to detect and to segment (ie delineate) regions of micro- and macro-metastases in

Increasing deep learning melanoma classification by classical and expert knowledge based image transforms
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Skin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnoses worldwide. Early detection influences the outcome of the disease and motivates our work. We obtain the state of the art results for the

A Novel Approach For Tumor Image Set Classification Based On Multi-Manifold Deep Metric Learning
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Abstract In digital era, Big Data and Deep learning are two rapidly growing hot trends. In recent years, we have witnessed major and noticeable advances in classification and representation learning brought by Deep learning techniques and/or algorithms. Deep

Deep learning based conventional neural network architecture for medical image classification
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The enactment of automatic medial image taxonomy using customary methods of machine learning and data mining mostly depend upon option of significant descriptive characteristics obtained from the medical images. Reorganization of those skins obliges

Deep feature learning for hyperspectral image classification and land cover estimation
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The differences in spatial sampling between field measurements and remote-sensing imagery can hinder the exploitation of contemporary data. When the field-based sampling is higher than airborne and spaceborne imagery, each pixel is naturally associated with

Deep Learning and SVM Classification for Plant Recognition in Content-Based Large Scale Image Retrieval.
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The PlantCLEF 2016 challenge focused on tree, herb and fern species identification based on different types of images. The aim of the task was to classify the plants in the images to species and to give a confidence score depicting the probability that a prediction is true. We

Deep learning and transfer learning approaches for image classification
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Women Deep Learning is-one of the machine learning areas, applied in recent areas. Various techniques have been proposed depends on varieties of learning , including unsupervised, semi-supervised, and supervised- learning . Some of the experimental results

Deep Learning versus Gist Descriptors for Image -based Malware Classification .
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Image features known as gist descriptors have recently been applied to the malware classification problem. In this research, we implement, test, and analyze a malware score based on gist descriptors, and verify that the resulting score yields very strong classification

Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network.
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Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in Convolutional Neural Networks (CNNs) considerably advance the emotion classification , which aims to distinguish

The classification of synthetic aperture radar image target based on deep learning
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In recent years, Deep learning is used to achieve its efficient performance in tasks such as object perception and image target recognition. Multilayer neural network model is one of the most basic and wide-used learning models in deep learning algorithm. The SAR

Image Classification Using Deep Learning
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Image Classification nowadays is used to narrow the gap between the computer vision and human vision so that the images can be recognized by machines in the same way as we humans do. It deals with assigning the appropriate class for the given image . We therefore

Image Classification of High Resolution Satellite Imagery Using Deep Learning Approach
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Extracting the information from earths surface is main goal of remote sensing. There are various artificial techniques which are helpful for extracting information. Compared to those artificial methods, this paper represents method which learns the features naturally

Supervised Deep Learning for Multi-Class Image Classification
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Abstract Multi-Class Image Classification is a big research topic with broad application prospects in Artificial Intelligence field nowadays. This course project describes the supervised machine learning methods, Convolutional Neural Networks (aka CNNs) along

A study on Image Classification based on Deep Learning and Tensorflow
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This research study about image classification by using the deep neural network (DNN) or also known as Deep Learning by using framework TensorFlow. Python is used as a programming language because it comes together with TensorFlow framework. The input

New deep kernel learning based models for image classification
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Deep learning system is used for solving many problems in different domains but it gives an over-fitting risk when richer representations are increased. In this paper, three different models with different deep multiple kernel learning architectures are proposed and