The present paper describes a query-by-sketch image retrieval system aimed at reducing the semantic gap by adopting relevance feedback. To reduce the semantic gap between low-level visual features and high-level semantics, in this content-based image retrieval system, users' sketches play an important role in relevance feedback. When users mark similar images of output images with "relevant" labels, the "relevant" images are relevant to the sketch image in positive feedback. This method was applied to 5,500 images in Corel Photo Gallery. Experimental results show that the proposed method is effective in retrieving images.


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    Title :

    Query-by-sketch image retrieval using relevance feedback


    Contributors:

    Conference:

    Optomechatronic Machine Vision ; 2005 ; Sapporo,Japan


    Published in:

    Publication date :

    2005-12-05





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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