Relevance Optimization in Image Database Using Feature Space Preference Mapping and Particle Swarm Optimization

Authors:
Mayuko Okayama;Nozomi Oka;Keisuke Kameyama
Affiliations:
Department of Risk Engineering, Graduate School of Systems and Information Engineering, University of Tsukuba,;Department of Risk Engineering, Graduate School of Systems and Information Engineering, University of Tsukuba,;Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan 305-8573
Venue:
Neural Information Processing
Year:
2008

Citing 4
Cited 0

Photobook: content-based manipulation of image databases

International Journal of Computer Vision
The SR-tree: an index structure for high-dimensional nearest neighbor queries

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Content-based query of image databases: inspirations from text retrieval

Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Self-Organizing Maps

Self-Organizing Maps

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two methods for retrieval relevance optimization using the user's feedback is proposed for a content-based image retrieval (CBIR) system. First, the feature space used in database image clustering for coarse classification is transferred to a preference feature spaceaccording to the user's feedback by a map generated by supervised training, thereby enabling to collect user-preferred images in the matching candidates. Second, the parameters in the fine-matching relaxation operation is optimized according to the user's evaluation of the retrieved image ranking using Particle Swarm Optimization. In the experiments, it is shown that the retrieval rankings are improved suiting the user's preference when feature space mapping and parameter optimization are used.