Learning in content-based image retrieval

T. S. Huang; Xiang Sean Zhou; M. Nakazato; Ying Wu; I. Cohen

doi:10.1109/DEVLRN.2002.1011829

Learning in content-based image retrieval

T. S. Huang, Xiang Sean Zhou, M. Nakazato, Ying Wu, I. Cohen

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

Abstract

In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.

Original language	English (US)
Title of host publication	Proceedings - 2nd International Conference on Development and Learning, ICDL 2002
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	155-162
Number of pages	8
ISBN (Electronic)	0769514596, 9780769514598
DOIs	https://doi.org/10.1109/DEVLRN.2002.1011829
State	Published - 2002
Event	2nd International Conference on Development and Learning, ICDL 2002 - Cambridge, United States Duration: Jun 12 2002 → Jun 15 2002

Publication series

Name	Proceedings - 2nd International Conference on Development and Learning, ICDL 2002

Other

Other	2nd International Conference on Development and Learning, ICDL 2002
Country/Territory	United States
City	Cambridge
Period	6/12/02 → 6/15/02

Funding

Acknowledgements: This work was supported in part by NSF Grant CDA 96-24396 and EIA 99-75019.

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1109/DEVLRN.2002.1011829

Cite this

Huang, T. S., Zhou, X. S., Nakazato, M., Wu, Y., & Cohen, I. (2002). Learning in content-based image retrieval. In Proceedings - 2nd International Conference on Development and Learning, ICDL 2002 (pp. 155-162). Article 1011829 (Proceedings - 2nd International Conference on Development and Learning, ICDL 2002). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEVLRN.2002.1011829

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title = "Learning in content-based image retrieval",

abstract = "In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.",

author = "Huang, \{T. S.\} and Zhou, \{Xiang Sean\} and M. Nakazato and Ying Wu and I. Cohen",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 2nd International Conference on Development and Learning, ICDL 2002 ; Conference date: 12-06-2002 Through 15-06-2002",

year = "2002",

doi = "10.1109/DEVLRN.2002.1011829",

language = "English (US)",

series = "Proceedings - 2nd International Conference on Development and Learning, ICDL 2002",

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Huang, TS, Zhou, XS, Nakazato, M, Wu, Y & Cohen, I 2002, Learning in content-based image retrieval. in Proceedings - 2nd International Conference on Development and Learning, ICDL 2002., 1011829, Proceedings - 2nd International Conference on Development and Learning, ICDL 2002, Institute of Electrical and Electronics Engineers Inc., pp. 155-162, 2nd International Conference on Development and Learning, ICDL 2002, Cambridge, United States, 6/12/02. https://doi.org/10.1109/DEVLRN.2002.1011829

Learning in content-based image retrieval. / Huang, T. S.; Zhou, Xiang Sean; Nakazato, M. et al.
Proceedings - 2nd International Conference on Development and Learning, ICDL 2002. Institute of Electrical and Electronics Engineers Inc., 2002. p. 155-162 1011829 (Proceedings - 2nd International Conference on Development and Learning, ICDL 2002).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.

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Huang TS, Zhou XS, Nakazato M, Wu Y, Cohen I. Learning in content-based image retrieval. In Proceedings - 2nd International Conference on Development and Learning, ICDL 2002. Institute of Electrical and Electronics Engineers Inc. 2002. p. 155-162. 1011829. (Proceedings - 2nd International Conference on Development and Learning, ICDL 2002). doi: 10.1109/DEVLRN.2002.1011829

Learning in content-based image retrieval

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