Title: Perceptual Uniform Descriptor and Ranking on Manifold for Image Retrieval
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| Authors: Liu, SL; Wu, J; Feng, L; Qiao, H; Liu, Y; Luo, WB; Wang, W
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| Author Full Names: Liu, Shenglan; Wu, Jun; Feng, Lin; Qiao, Hong; Liu, Yang; Luo, Wenbo; Wang, Wei
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| Source: INFORMATION SCIENCES, 424 235-249; 10.1016/j.ins.2017.10.010 JAN 2018
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| Language: English
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| Abstract: Incompatibility of image descriptor and ranking has been often neglected in image retrieval. In this paper, Manifold Learning and Gestalt Psychology Theory are involved to solve the problem of incompatibility. A new holistic descriptor called Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed, which combines color and gradient direction to imitate human visual uniformity. PUD features in the same class images distributes on one manifold in most cases, as PUD improves the visual uniformity of the traditional descriptors. Thus, we use manifold ranking and PUD to realize image retrieval. Experiments were carried out on four benchmark data sets, and the proposed method is shown to greatly improve the accuracy of image retrieval. Our experimental results in Uk-bench and Corel-1K datasets demonstrate that N-S score reached 3.58 (HSV 3.4) and mAP at 81.77% (ODBTC 77.9%) respectively by utilizing PUD which has only 280 dimensions. The results are higher than other holistic image descriptors including local ones as well as state-of-the-arts retrieval methods. (C) 2017 Elsevier Inc. All rights reserved.
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| ISSN: 0020-0255
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| eISSN: 1872-6291
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| IDS Number: FM3FO
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| Unique ID: WOS:000414889900014
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