NDLI: Informative patches sampling for image classification by utilizing bottom-up and top-down information

Content Provider	Springer Nature Link
Author	Bai, Shuang Matsumoto, Tetsuya Takeuchi, Yoshiri Kudo, Hiroaki Ohnishi, boru
Copyright Year	2013
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 low-level 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 bottom-up information through processing low-level image information and top-down 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 bottom-up information and/or top-down 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.
Starting Page	959
Ending Page	970
Page Count	12
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	24
Issue Number	5
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2013-01-05
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image classification Patch sampling Bottom-up Top-down Saliency Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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