Color-Based Image Retrieval from High-Similarity Image Databases

  • Michael Edberg Hansen
  • Jens Michael Carstensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


Many image classification problems can fruitfully be thought of as image retrieval in a “high similarity image database” (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition.


Image Retrieval Image Database Retrieval Performance Fungal Coloni Convex Linear Combination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Michael Edberg Hansen
    • 1
  • Jens Michael Carstensen
    • 1
  1. 1.Image Processing and Computer VisionInformatics and Mathematical ModellingLyngbyDenmark

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