A learning state-space model for image retrieval

Authors:
Cheng-Chieh Chiang;Yi-Ping Hung;Greg C. Lee
Affiliations:
Department of Information and Computer Education, College of Education, National Taiwan Normal University, Taipei, Taiwan and Department of Information Technology, Takming College, Taipei, Taiwan;Graduate Institute of Networking and Multimedia, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, College of Science, National Taiwan Normal University, Taipei, Taiwan
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2007

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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.