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Volume 25 Issue 10
Oct.  2003
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Gao Yongying, Zhang Yujin, Luo Yun. Image Retricval System Based on semantic features of objects[J]. Journal of Electronics & Information Technology, 2003, 25(10): 1341-1348.
Citation: Gao Yongying, Zhang Yujin, Luo Yun. Image Retricval System Based on semantic features of objects[J]. Journal of Electronics & Information Technology, 2003, 25(10): 1341-1348.

Image Retricval System Based on semantic features of objects

  • Received Date: 2002-02-18
  • Rev Recd Date: 2003-03-10
  • Publish Date: 2003-10-19
  • Most existing content-based image retrieval systems using low-level features that could not describe high-level semantics thoroughly and accurately. In this paper, a novel system for content-based image retrieval is designed and created, which combines image semantics based on a multi-level model for image description. In this image description model, image contents could be analyzed and represented through different levels and the transition from low-level features to high-level semantics is thus achieved. Corresponding querying mechanism and feedback are also proposed based on this image model. Aiming at object semantics in image, this querying mechanism is much closer to human beings understanding of image contents so that it provides a convenient and effective querying procedure. The feedback used in the system is a self-adaptive relevance feedback based on object descriptions, it permits to propose different querying schemes according to the different demands raised by various users, and thus optimal results could be refined.
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  • Y. Rui, T. S. Huang, S. Mehrotra, Relevance feedback techniques in interactive content-based image retrieval, 1998, SPIE 3312: 25-34.[2]D.Z. Hong, J. K. Wu, S. S. Singh, Refining image retrieval based on context-driven method,1999, SPIE 3656: 581-593.[3]A. Jaimes, S. F. Chang, Model-based classification of visual information for content-based retrieval, SPIE 3656: 402-414.[4]E.J. Pauwels, G. Frederix, Finding salient regions in images: Nonparametric clustering for image segmentation and grouping, Computer Vision and Image Understanding, 1999, 75(1): 73-85.[5]Y.Y. Gao, Y. J. Zhang, Object classification using mixed color feature, Proc. ICASSP, Istanbul,2000, 4: 2003-2006.[6]S.G. Mallat, Multifrequency channel decompositions of images and wavelet models, IEEE Trans.on ASSP, 1989, ASSP-37(12): 2091 2110.[7]Y.Y. Gao.[J].Y. J. Zhang, N. S. Merzlyakov, Semantic-based image description model and its implementation in image retrieval, Proc. of ICIG2000, Tianjin.2000,:-[8]高永英,章毓晋,基于多级描述模型的渐近式图像内容理解,电子学报,2001,29(10):1376-1380.[9]G. Ciocca.[J].R. Schettini, Using a relevance feedback mechanism to improve content-based image retrieval, Proc. of 3rd VISUAL99, Amsterdam.1999,:-[10]梅镇彤,学习和记忆的神经生物学,上海,上海科学技术出版社,1997,51-53.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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