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A Learning State-Space Model for Image Retrieval

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.

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Correspondence to Cheng-Chieh Chiang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chiang, C., Hung, Y. & Lee, G.C. A Learning State-Space Model for Image Retrieval. EURASIP J. Adv. Signal Process. 2007, 083526 (2007). https://doi.org/10.1155/2007/83526

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Keywords

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