NDLI: Kernelized pyramid nearest-neighbor search for object categorization

Content Provider	Springer Nature Link
Author	Cheng, Hong Yu, Rongchao Liu, Zicheng Yang, Lu Chen, Xue wen
Copyright Year	2014
Abstract	Nearest-neighbor-based image classification has drawn considerable attention in the past several years thanks to its simplicity and efficiency. Recently, a Kernelized version of Naive-Bayes Nearest-Neighbor (KNBNN) approach has been proposed to combine Nearest-Neighbor-based approaches with other bag-of-feature (BoF) based kernels. However, similar to an orderless BoF image representation, the KNBNN ignores global geometric correspondence. In this paper, our contributions are threefolded. First, we present a technique to exploit the global geometric correspondence in a kernelized NBNN classifier framework. We divide an image into increasingly fine sub-regions like the spatial pyramid matching (SPM) approach; Second, we introduce a pyramid nearest-neighbor kernel by measuring the local similarity in each pyramid window. Third, for better calibrating the outputs of each window, we fit a sigmoid function to add posterior probability to its SVM outputs, and then weight these outputs of all windows. The sigmoid parameters and weight values are learned in a class-dependent and window-dependent manner. By doing so, we learn a class-specific geometric correspondence. Finally, the proposed approach is evaluated on two public datasets: Scene-15 and Caltech-101. We reach 85.2 % recognition rate on Scene-15 and 73.3 % on Caltech-101 only using single descriptor. The experimental results show that our approach significantly outperforms existing techniques.
Starting Page	931
Ending Page	941
Page Count	11
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	25
Issue Number	4
e-ISSN	14321769
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-04-03
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Local kernels Naive-Bayes Nearest Neighbor Spatial pyramid matching Object categorization 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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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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