Content Based Image Retrieval Using Quantitative Semantic Features

  • Anuja Khodaskar
  • Siddharth Ladhake
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)


Retrieval of images based on low level visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. In order to improve the effectiveness of content-based image retrieval systems, research direction has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. In this paper, the framework for Content-Based Image Retrieval system Fuzzy Logic approach is proposed to bridge the semantic gap between low level features and high-level semantic features with the aim to optimize the performance of CBIR systems.


CBIR Quantitative Semantic Features Fuzzy Color Histogram 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anuja Khodaskar
    • 1
  • Siddharth Ladhake
    • 1
  1. 1.Sipnas’ College of Engineering & TechnologyAmravatiIndia

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