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  • Research Article
  • Open Access

A Learning State-Space Model for Image Retrieval

EURASIP Journal on Advances in Signal Processing20072007:083526

  • Received: 30 August 2006
  • Accepted: 12 March 2007
  • Published:


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.


  • Information Technology
  • Feature Space
  • Region Scale
  • Quantum Information
  • Image Segmentation

Authors’ Affiliations

Department of Information and Computer Education, College of Education, National Taiwan Normal University, Taipei, 106, Taiwan
Department of Information Technology, Takming College, Taipei, 114, Taiwan
Graduate Institute of Networking and Multimedia, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, 106, Taiwan
Department of Computer Science and Information Engineering, College of Science, National Taiwan Normal University, Taipei, 106, Taiwan


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© Cheng-Chieh Chiang et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.