Title

Query Decomposition: A Multiple Neighborhood Approach To Relevance Feedback Processing In Content-Based Image Retrieval

Abstract

Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k-NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm - the Query Decomposition model -that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval. © 2006 IEEE.

Publication Date

10-17-2006

Publication Title

Proceedings - International Conference on Data Engineering

Volume

2006

Number of Pages

84-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICDE.2006.123

Socpus ID

33749645426 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33749645426

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