We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features of: histograms in the Ohta color space; multiresolution, simultaneous autoregressive model parameters; and coefficients of a shift-invariant DCT. We demonstrate that performance is improved by computing features on subblocks, classifying these subblocks, and then combining these results in a way reminiscent of stacking. State of the art single-feature methods are shown to result in about 75-86% performance, while the new method results in 90.3% correct classification, when evaluated on a diverse database of over 1300 consumer images provided by Kodak.


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    Title :

    Indoor-outdoor image classification


    Contributors:
    Szummer, M. (author) / Picard, R.W. (author)


    Publication date :

    1998-01-01


    Size :

    1898018 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Indoor-Outdoor Image Classification

    Szummer, M. / Picard, R. W. / IEEE; Computer Society; Technical Committee on Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1998


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