NDLI: Label diagnosis through self tuning for web image search

Content Provider	IEEE Xplore Digital Library
Author	Jun Wang Yu-Gang Jiang Shih-Fu Chang
Copyright Year	2009
Description	Author affiliation: Dept. of Electr. Eng., Columbia Univ., New York, NY, USA (Jun Wang; Yu-Gang Jiang; Shih-Fu Chang)
Abstract	Semi-supervised learning (SSL) relies on partial supervision information for prediction, where only a small set of samples are associated with labels. Performance of SSL is significantly degraded if the given labels are not reliable. Such problems arise in realistic applications such as Web image search using noisy textual tags. This paper proposes a novel and efficient graph based SSL method with the unique capacity of pruning contradictory labels and inferring new labels through a bidirectional and alternating optimization process. The objective is to automatically identify the most suitable samples for manipulation, labeling or unlabeling, and meanwhile estimate a smooth classification function over a weighted graph. Different from other graph based SSL approaches, the proposed method employs a bivariate objective function and iteratively modifies label variables on both labeled and unlabeled samples. Starting from such a SSL setting, we present a relearning framework to improve the performance of base learner, particularly for the application of Web image search. Besides the toy demonstration on artificial data, we evaluated the proposed method on flicker image search with unreliable textual labels. Experimental results confirm the significant improvements of the method over the baseline text based search engine and the state-of-the-art SSL methods.
Starting Page	1390
Ending Page	1397
File Size	3260254
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training data Semisupervised learning Labeling Filters Degradation Optimization methods Search engines Supervised learning Data acquisition Geometry
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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