Synopsis
Image classification, including object recognition and scene classification, remains to be a major challenge to the computer vision community. As machine can be able to extract information from an image and classify it in order to solve some tasks. Recently SVMs using Spatial Pyramid Matching (SPM) kernel have been highly successful in image classification. Despite its popularity, this technique cannot handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing Vector Quantization to Sparse Coding followed by multi-scale Spatial Max Pooling, and also propose a large scale linear classifier based on Scale Invariant Feature Transform (SIFT) and Sparse Codes. This new adapted algorithm remarkably can handle thousands of training images and classify them into different categories.
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About the Author
Mostafa Ibrahim elkhalil mostafa labib , Nationality: Egyptiangraduated from Faculty of computing and information Technology, Computer Science,2006Diploma in E-Business from ITI,2007Master in Computer Science from AASTMT,2013Current job is Multimedia Developer in IT Department,CULTNAt,Bibliotheca Alexandrina.
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Large Scale Linear Coding for Image Classification
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Image classification, including object recognition and scene classification, remains to be a major challenge to the computer vision community. As machine can be able to extract information from an image and classify it in order to solve some tasks. Recently SVMs using Spatial Pyramid Matching (SPM) kernel have been highly successful in image classification. Despite its popularity, this technique cannot handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing Vector Quantization to Sparse Coding followed by multi-scale Spatial Max Pooling, and also propose a large scale linear classifier based on Scale Invariant Feature Transform (SIFT) and Sparse Codes. This new adapted algorithm remarkably can handle thousands of training images and classify them into different categories. 144 pp. Englisch. Seller Inventory # 9783659551352
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Labib MostafaMostafa Ibrahim elkhalil mostafa labib , Nationality: Egyptiangraduated from Faculty of computing and information Technology, Computer Science,2006Diploma in E-Business from ITI,2007Master in Computer Science from AASTM. Seller Inventory # 5164243
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Large Scale Linear Coding for Image Classification
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Image classification, including object recognition and scene classification, remains to be a major challenge to the computer vision community. As machine can be able to extract information from an image and classify it in order to solve some tasks. Recently SVMs using Spatial Pyramid Matching (SPM) kernel have been highly successful in image classification. Despite its popularity, this technique cannot handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing Vector Quantization to Sparse Coding followed by multi-scale Spatial Max Pooling, and also propose a large scale linear classifier based on Scale Invariant Feature Transform (SIFT) and Sparse Codes. This new adapted algorithm remarkably can handle thousands of training images and classify them into different categories.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch. Seller Inventory # 9783659551352
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Large Scale Linear Coding for Image Classification
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image classification, including object recognition and scene classification, remains to be a major challenge to the computer vision community. As machine can be able to extract information from an image and classify it in order to solve some tasks. Recently SVMs using Spatial Pyramid Matching (SPM) kernel have been highly successful in image classification. Despite its popularity, this technique cannot handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing Vector Quantization to Sparse Coding followed by multi-scale Spatial Max Pooling, and also propose a large scale linear classifier based on Scale Invariant Feature Transform (SIFT) and Sparse Codes. This new adapted algorithm remarkably can handle thousands of training images and classify them into different categories. Seller Inventory # 9783659551352
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