基于多特征绑定的近似重复图像检索<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201610008

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Abstract The bundle feature based method is effective for near-duplicate image retrieval. However, there are some limitations due to the use of SIFT descriptor and coordinate information. Therefore, in this paper color features are integrated into traditional SIFT representation to form the multi-features bundle and two kinds of robust orders between points in bundle feature are introduced-the circle order and the main orientation order. Firstly, the color distribution features of interesting points are extracted and indexing is embedded into them, and then the potential false matchings are discarded by the Kullback-Leibler divergence of corresponding color features. Secondly, the geometric loss score is computed according to the inconsistency between orders of the distance from points to the center of bundle region or the orders of the angle between the orientation of points and main orientation of bundle. The experiment on Copydays dataset shows the improved retrieval performance by adding color features and better circle order and main orientation order geometric constraints of the bundle feature in the image retrieval task.

Key words： Near-Duplicate Image Retrieval Bundle Feature Order Information

Received: 02 March 2016

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China (No.61373060)

About author:: (SONG Ge, born in 1991, master student. His research inte-rests include image retrieval and computer vision.)
(JIN Xin, born in 1987, Ph.D. candidate. His research inte-rests include pattern recognition and computer vision.)
(TAN Xiaoyang(Corresponding author), born in 1971, Ph.D., professor. His research interests include face recognition, machine learning, pattern recognition and computer vision.)

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	SONG Ge
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	TAN Xiaoyang

Cite this article:

SONG Ge,JIN Xin,TAN Xiaoyang. Near-Duplicate Image Retrieval Based on Multi-features Bundle[J]. , 2016, 29(10): 943-950.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201610008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I10/943

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