NDLI: Multi-feature metric learning with knowledge transfer among semantics and social tagging

Content Provider	IEEE Xplore Digital Library
Author	Shuhui Wang Shuqiang Jiang Qingming Huang Qi Tian
Copyright Year	2012
Description	Author affiliation: Dept. of Computer Science, Univ. of Texas at San Antonio, TX78249, U.S.A. (Qi Tian) \|\| Key Lab of Intell. Info. Process.(CAS), Inst. of Comput. Tech., CAS, Beijing, 100190, China (Shuhui Wang; Shuqiang Jiang; Qingming Huang)
Abstract	Previous metric learning approaches learn a unified metric for all the classes on single feature representation, thus cannot be directly transplanted to applications involving multiple features, hundreds to thousands of hierarchical structured semantics and abundant social tagging. In this paper, we propose a novel multi-task multi-feature metric learning method which models the information sharing mechanism among different learning tasks. We decompose the real world multi-class problems such as semantic categorization or automatic tagging into a set of tasks where each task corresponds to several classes with strong visual correlation. We conduct metric learning to learn a set of (hyper)category-specific metrics for all the tasks. By encouraging model sharing among tasks, more generalization power is acquired. Another advantage is the capability of simultaneous learning with semantic information and social tagging based on the multi-task learning framework, and thus they both benefit from the information provided by each other. Experiments demonstrate the advantages on applications including semantic categorization and automatic tagging compared with other popular metric learning approaches.
Starting Page	2240
Ending Page	2247
File Size	459638
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247933
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Kernel Semantics Tagging Training Visualization Support vector machines
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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