A Learning State-Space Model for Image Retrieval. (30th April 2007)

• Record Type: Journal Article
• Authors: Chiang Chiang, Cheng-Chieh Cheng-Chieh; Hung Hung, Yi-Ping Yi-Ping; Lee Lee, Greg C. Greg C.
• Other Names: Izquierdo Izquierdo Ebroul Ebroul Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-04-30
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/83526
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.