Minimizing user interaction by automatic and semi-automatic relevance feedback for image retrieval

This paper describes the unsupervised-interactive learning method, using SOTM architecture, for the automation of relevance feedback (RF) in content-based image retrieval. The SOTM is shown to exhibit good behavior in relevance classification; providing a possible solution to minimizing user interactions in both fully automatic and semi-automatic domains, while achieving high retrieval accuracy in the context of adaptive retrieval. Computer simulation shows this system is very effective when applied to compressed domain retrieval systems for texture retrieval and the JPEG photograph database applications.