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Title of Journal: Machine Vision and Applications

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Abbravation: Machine Vision and Applications

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Springer-Verlag

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DOI

10.1016/0009-3084(93)90060-g

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1432-1769

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Informative patches sampling for image classificat

Authors: Shuang Bai Tetsuya Matsumoto Yoshinori Takeuchi Hiroaki Kudo Noboru Ohnishi
Publish Date: 2013/01/05
Volume: 24, Issue: 5, Pages: 959-970
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Abstract

In image classification based on bag of visual words framework image patches used for creating image representations affect the classification performance significantly However currently patches are sampled mainly based on processing lowlevel image information or just extracted regularly or randomly These methods are not effective because patches extracted through these approaches are not necessarily discriminative for image categorization In this paper we propose to utilize both bottomup information through processing lowlevel image information and topdown information through exploring statistical properties of training image grids to extract image patches In the proposed work an input image is divided into regular grids each of which is evaluated based on its bottomup information and/or topdown information Subsequently every grid is assigned a saliency value based on its evaluation result so that a saliency map can be created for the image Finally patch sampling from the input image is performed on the basis of the obtained saliency map Furthermore we propose a method to fuse these two kinds of information The proposed methods are evaluated on both object categories and scene categories Experiment results demonstrate their effectiveness

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