Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing

J. Yang, Qing Li, Y. Zhuang

Research output: Journal article publicationLiterature reviewAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Adaptation to the characteristics of specific images and the preferences of individual users is critical to the success of an image retrieval system but insufficiently addressed by the existing approaches. In this paper, we propose an elegant and effective approach to data-adaptive and user-adaptive image retrieval based on the idea of peer indexing - describing an image through semantically relevant peer images. Specifically, we associate each image with a two-level peer index that models the "data characteristics" of the image as well as the "user characteristics" of individual users with respect to this image. Based on two-level image peer indexes, a set of retrieval parameters including query vectors and similarity metric are optimized towards both data and user characteristics by applying the pseudo feedback strategy. A cooperative framework is proposed under which peer indexes and image visual features are integrated to facilitate data- and user-adaptive image retrieval. Simulation experiments conducted on real-world images have verified the effectiveness of our approach in a relatively restricted setting.
Original languageEnglish
Pages (from-to)47-63
Number of pages17
JournalInternational Journal of Computer Vision
Volume56
Issue number1-2
DOIs
Publication statusPublished - 1 Jan 2004
Externally publishedYes

Keywords

  • Content-based image retrieval
  • Data-adaptive
  • Peer indexing
  • Pseudo feedback
  • User-adaptive

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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