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    » Feature selection for content-based image retrieval
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    ICPR
    2000
    IEEE
    153views computer vision» more  ICPR 2000»
    Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
    14 years 8 months ago
    Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
    Download  www.cs.okstate.edu
    Probabilistic feature relevance learning (PFRL) is an effective technique for adaptively computing local feature relevance for content-based image retrieval. It however becomes le...
    Douglas R. Heisterkamp, Jing Peng, H. K. Dai
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    KDD
    2000
    ACM
    116views Data Mining» more  KDD 2000»
    Learning Feature Weights from User Behavior in Content-Based Image Retrieval
    13 years 11 months ago
    Learning Feature Weights from User Behavior in Content-Based Image Retrieval
    Download  www.sim.hcuge.ch
    Henning Müller, Wolfgang Müller 0002, Da...
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    JACIII
    2007
    72views more  JACIII 2007»
    A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval
    13 years 7 months ago
    A Feature Vector Approach for Inter-Query Learning for Content-Based Image Retrieval
    Download  www.fujipress.jp
    Kien-Ping Chung, Chun Che Fung
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    PAMI
    2002
    125views more  PAMI 2002»
    A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
    13 years 7 months ago
    A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
    Download  wang.ist.psu.edu
    Yixin Chen, James Ze Wang
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    CSIE
    2009
    IEEE
    214views Theoretical Computer Science» more  CSIE 2009»
    Evaluating Clustering Algorithms: Cluster Quality and Feature Selection in Content-Based Image Clustering
    14 years 2 months ago
    Evaluating Clustering Algorithms: Cluster Quality and Feature Selection in Content-Based Image Clustering
    Download  www.sics.se
    The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Ward’s method, with the latter three being different hierarchical...
    Mesfin Sileshi, Björn Gambäck
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