Share Email Print

Proceedings Paper

Framework for image mining and retrieval
Author(s): Rokia Missaoui; Madenda Sarifuddin; Youssef Hamouda; Jean Vaillancourt; Hayet Laggoune
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we describe a three-step content-based approach to image retrieval and mining. At a first step, visual features such as color and shape are generated from images by improving a few existing feature extraction techniques. Then, both visual features and descriptive data (i.e., metadata) are considered for a coarse-grain similarity analysis using a conceptual clustering approach called formal concept analysis (concept lattice theory). This approach is designed and implemented so that exploratory mechanisms such as browsing, zooming/shrinking and visualization allow the user to discover and refine the cluster which is the most appropriate to his/her target image. At this second stage, issues such as dimension reduction, cluster construction and association rule generation are handled. The last step consists to conduct a fine-grain similarity analysis on some selected cluster(s) identified at the second stage by using two newly proposed similarity measures.

Paper Details

Date Published: 23 June 2003
PDF: 9 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.503300
Show Author Affiliations
Rokia Missaoui, Univ. du Quebec en Outaouais (Canada)
Madenda Sarifuddin, Univ. du Quebec a Montreal (Canada)
Youssef Hamouda, Univ. du Quebec a Montreal (Canada)
Jean Vaillancourt, Univ. du Quebec en Outaouais (Canada)
Hayet Laggoune, Univ. du Quebec a Montreal (Canada)

Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

© SPIE. Terms of Use
Back to Top