Integrating unlabeled images for image retrieval based on relevance feedback

Ying Wu, Qi Tian, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Retrieval techniques based on pure similarity metrics are often suffered from the scales of image features. An alternative approach is to learn a mapping based on queries and relevance feedback by supervised learning. However, the learning is plagued by the insufficiency of labeled training images. Different from most current research in image retrieval, this paper investigates the possibility of taking advantage of unlabeled images in the given image database to make a hybrid statistical learning feasible. Assuming a generative model of the database, the proposed approach casts image retrieval as a transductive learning problem in a probabilistic framework. Our experiments show that the proposed approach has a satisfactory performance in image retrieval applications.

Original languageEnglish (US)
Title of host publicationProceedings - 15th International Conference on Pattern Recognition, ICPR 2000 - Volume 1
Subtitle of host publicationComputer Vision and Image Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-24
Number of pages4
ISBN (Electronic)0769507506
DOIs
StatePublished - 2000
Event15th International Conference on Pattern Recognition, ICPR 2000 - Barcelona, Spain
Duration: Sep 3 2000Sep 7 2000

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

Other15th International Conference on Pattern Recognition, ICPR 2000
Country/TerritorySpain
CityBarcelona
Period9/3/009/7/00

Funding

This work was supported in part by National Science Foundation Grants CDA-96-24396, IRI-96-34618 and EIA-99-75019.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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