Intelligent Concept-Oriented and Content-Based Image Retrieval by using data mining and query decomposition techniques
Vincent S. M. Tseng, Ja-Hwung Su, Haohua Ku, Bowen Wang · 2008
Traditional image retrieval based on visual-based matching is not effective in multimedia applications. Consequently, the modeling of high-level human sense for image retrieval has been a challenging issue over the past few years. In fact, the concepts hidden in the images play key roles in semantic image retrieval. In this paper, we propose a novel method named Intelligent Concept-Oriented Search (ICOS) that can capture the high-level concepts in images by utilizing data mining and query decomposition techniques. The contributions of the proposed method lie in that we provide: 1) effective annotation for conceptual objects, 2) association mining for conceptual objects, 3) visual ranking for conceptual objects and 4) intelligent search method for enhancing high-level concept image retrieval. Through experimental evaluations, ICOS is shown to be very effective and efficient in capturing the implicit high-level concepts for image retrieval.