NDLI: Multi-graph multi-instance learning with soft label consistency for object-based image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Fei Li Rujie Liu
Copyright Year	2015
Description	Author affiliation: Fujitsu R&D Center Co., Ltd., Beijing, China (Fei Li; Rujie Liu)
Abstract	Object-based image retrieval has been an active research topic in the last decade, in which a user is only interested in some object instead of the whole image. As a promising approach, graph-based multi-instance learning has been paid much attention. Early retrieval methods often conduct learning on one graph in either image or region level. To further improve the performance, some recent methods adopt multi-graph learning, but the relationship between image- and region-level information is not well explored. In this paper, by constructing both image- and region-level graphs, a novel multi-graph multi-instance learning method is proposed. Different from the existing methods, the relationship between each labeled image and its segmented regions is reflected by the consistency of their corresponding soft labels, and it is formulated by the mutual restrictions in an optimization framework. A comprehensive cost function is designed to involve all the available information, and an iterative solution is introduced to solve the problem. Experimental results on the benchmark data set demonstrate the effectiveness of our proposal.
Starting Page	1
Ending Page	6
File Size	5144071
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proposals Image segmentation Image retrieval Learning systems Cost function Quadratic programming multi-instance learning Object-based image retrieval graph-based learning
Content Type	Text
Resource Type	Article

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