NDLI: Relative image similarity learning with contextual information for Internet cross-media retrieval

Content Provider	Springer Nature Link
Author	Jiang, Shuqiang Song, Xinhang Huang, Qingming
Copyright Year	2013
Abstract	With the fast explosive rate of the amount of image data on the Internet, how to efficiently utilize them in the cross-media scenario becomes an urgent problem. Images are usually accompanied with contextual textual information. These two heterogeneous modalities are mutually reinforcing to make the Internet content more informative. In most cases, visual information can be regarded as an enhanced content of the textual document. To make image-to-image similarity being more consistent with document-to-document similarity, this paper proposes a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted in this paper, and the ranking structure is established by comparing the relative relations of textual information. The learning results are in more accordance with the human’s recognition. The proposed method in this paper can be used not only for the image-to-image retrieval, but also for cross-modality multimedia, where a query expansion framework is proposed to get more satisfactory results. Extensive experimental evaluations on large scale Internet dataset validate the performance of the proposed methods.
Starting Page	645
Ending Page	657
Page Count	13
File Format	PDF
ISSN	09424962
Journal	Multimedia Systems
Volume Number	20
Issue Number	6
e-ISSN	14321882
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-02-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image similarity learning Structural SVM Cross-media retrieval Query expansion Multimedia Information Systems Computer Communication Networks Operating Systems Data Storage Representation Data Encryption Computer Graphics
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Media Technology Hardware and Architecture Software

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