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

文献类型：期刊论文

作者Hu, Weiming1; Xie, Nianhua1; Hu, Ruiguang1; Ling, Haibin2; Chen, Qiang3; Yan, Shuicheng4; Maybank, Stephen5
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2014-12-01
卷号36期号:12页码:2338-2352
关键词Histogram bin ratio histogram distance image classification
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
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记录号WOS:000344988000002
源URL[http://ir.ia.ac.cn/handle/173211/3272]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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