NDLI: Improving the Discriminability of Dictionary by Gist Information Detection

Content Provider	IEEE Xplore Digital Library
Author	Tao Xu Hongya Tuo Zheng Fang Li Liu Zhongliang Jing
Copyright Year	2013
Description	Author affiliation: Sch. of Aeronaut. & Astronaut., Shanghai Jiao Tong Univ., Shanghai, China (Tao Xu; Hongya Tuo; Zheng Fang; Li Liu; Zhongliang Jing)
Abstract	Image representations using code words from a visual dictionary are widely applied in object detection and categorization. Traditionally, there are two types of methods to construct a dictionary: k-means and optimization-based method. The former cannot achieve a good discriminability because it extracts too many background features. The latter needs to cooperate with coding methods and brings about high computational complexity. In this paper, we present an effective method based on Gist information detection to obtain a more discriminative dictionary with low computational cost. First, we partition the image into increasingly fine sub-regions, and calculate the Gist information of each region. Then extract more features from sub-regions with richer information and fewer features from ones with less information. Finally construct a dictionary using the non-uniform sampling features. Experiments on Caltech101 show that our method can achieve a better performance than traditionally k-means and the optimization-based method. Hence our dictionary has a better discrimination.
Starting Page	400
Ending Page	403
File Size	493996
Page Count	4
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.89
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization SPM Gist information Dictionaries SIFT dictionary Computational modeling Clustering algorithms Feature extraction Encoding Vectors
Content Type	Text
Resource Type	Article

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