NDLI: An Attribute-Assisted Reranking Model for Web Image Search

Content Provider	IEEE Xplore Digital Library
Author	Junjie Cai Zheng-Jun Zha Meng Wang Shiliang Zhang Qi Tian
Copyright Year	1992
Abstract	Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. In this paper, we propose a visual-attribute joint hypergraph learning approach to simultaneously explore two information sources. A hypergraph is constructed to model the relationship of all images. We conduct experiments on more than 1,000 queries in MSRA-MMV2.0 data set. The experimental results demonstrate the effectiveness of our approach.
Sponsorship	IEEE Signal Processing Society
Page Count	12
File Size	2658578
Starting Page	261
Ending Page	272
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Semantics Feature extraction Image edge detection Wheels Training Face Attribute-assisted Search Hypergraph
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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