A new relevance feedback technique for iconic image retrieval based on spatial relationships
Journal of Systems and Software
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
Relevance feedback for the Earth Mover's distance
AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
Multimedia Tools and Applications
Content-based image retrieval using OWA fuzzy linking histogram
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps.