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低层特征融合的图像检索算法设计

Design of Image Retrieval Algorithm With Low-level Feature Fusion
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摘要 现阶段基于单一的特征提取算法已不能满足人们对图像查准率、查全率的要求。有效地整合图像的颜色、纹理、形状等特征,使得各特征优势互补,进而提高系统检索效率现已成为一个值得研究并深入的课题[1]。从颜色、纹理两方面的特征出发,研究并设计了一整套的方案,很好的解决了查准率、查全率等问题。 At present,based on single feature extraction algorithm already cannot satisfy the requirements of precision rate and recall rate.The effective integration of image color,texture,shape characteristics,have complementary advantages,and improve the retrieval efficiency system that becomes a worthy of study and in-depth topic now.From color,texture characteristics,this paper researchs and designs a set of solutions that solve the precision rate and recall rate problems.
出处 《电脑开发与应用》 2013年第4期44-46,50,共4页 Computer Development & Applications
基金 福建省科技厅高校专项科研基金资助项目(JK2012026)
关键词 颜色特征 GLCM CBIR 特征融合 归一化 color features GLCM CBIR feature fusion normalization
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参考文献6

  • 1Henning Miiller, Wolf gang Miiller,Stephane Marchand-Maillet, et al A Framework for Benchmarking in CBIR [J].Muhimedia Tools and Applications, 2003,21 ( 1 ) : 1380-7501.
  • 2Nhu-Van Nguyen,Alain Boucher,Jean-Marc.Cluster-based relevance feedback for CBIR:a combination of query pointmovement and query expansion [J]. Journal of Ambient Intelligence and Humanized Computing, 2012 (4) : 211-215.
  • 3P S.suhasini,Dr K.sri Rama Krishna, Dr I.V Murali Krishna. Cbir Using Color Histogram Processing [J] Journal of Theoretical and Applied Information Technology,2009,6 (1): 1992-2003.
  • 4孙君顶赵珊.图像低层特征提取与检索技术[M].北京:电子工业出版社,2009:20-22.
  • 5M.Benco,R.Hudec. Novel Method for Color Textures Features Extraction Based on GLCM [J].Radio Engineering-Prague, 2007,16(4): 1210-1230.
  • 6Dipti Patra, Mridula J. Featured based Segmentation of Color Textured Images using GLCM and Markov Random Field Model [J]. Proceedings of World Academy of Science: Engineering Technology, 2011,(77 ) : 1307-6884.

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