This paper presents a new relevance feedback (RF) method for image retrieval in content-based image retrieval (CBIR). The main conception of the method gives two aspects: First logistic regression adjusts the weight of each element in features extracted from the images in database with the preferences of the user. Then following a Bayesian methodology, which yields the posteriori of the images in the database and used to show to the user a new set of images. The retrieval system is repeating until he/she is satisfied or the target image has been found. Experimental results show the superiority of the proposed method.


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    Title :

    A Novel Approach Based on Logistic Regression and Bayesian for Relevance Feedback in Content-Based Image Retrieval


    Contributors:
    Kong, Jun (author) / Wang, Xuefeng (author) / Liu, Zhen (author) / Zhang, Xiaohua (author) / Cui, Jingxia (author) / Zhang, Jingbo (author)


    Publication date :

    2008-05-01


    Size :

    318449 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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