Bin Ratio-Based Histogram Distances and Their Application to Image Classification

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	Bin Ratio-Based Histogram Distances and Their Application to Image Classification
	Hu, Weiming1 ; Xie, Nianhua 1; Hu, Ruiguang 1; Ling, Haibin 2; Chen, Qiang 3; Yan, Shuicheng 4; Maybank, Stephen 5
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2014-12-01
卷号	36 期号:12 页码:2338-2352
文章类型	Article
摘要	Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the l(1) histogram distance and the chi(2) histogram distance to generate the l(1) BRD and the chi(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the l(1) and chi(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.
关键词	Histogram Bin Ratio Histogram Distance Image Classification
WOS标题词	Science & Technology ; Technology
关键词[WOS]	NATURAL SCENE CATEGORIES ; OBJECT RECOGNITION ; KERNEL ; FEATURES ; RETRIEVAL ; TEXTURE ; SHAPE ; MODELS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000344988000002
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3272
专题	多模态人工智能系统全国重点实验室_视频内容安全
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA 3.IBM Res, Carlton, Vic 3053, Australia 4.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore 5.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, Berks, England
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Hu, Weiming,Xie, Nianhua,Hu, Ruiguang,et al. Bin Ratio-Based Histogram Distances and Their Application to Image Classification[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014,36(12):2338-2352.
APA	Hu, Weiming.,Xie, Nianhua.,Hu, Ruiguang.,Ling, Haibin.,Chen, Qiang.,...&Maybank, Stephen.(2014).Bin Ratio-Based Histogram Distances and Their Application to Image Classification.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,36(12),2338-2352.
MLA	Hu, Weiming,et al."Bin Ratio-Based Histogram Distances and Their Application to Image Classification".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 36.12(2014):2338-2352.