Indoor-Outdoor Image Classification
نویسندگان
چکیده
We show how high-level scene properties can be inferred from classiication of low-level image features, speciically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) his-tograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model parameters (3) coef-cients of a shift-invariant DCT. We demonstrate that performance is improved by computing features on sub-blocks, 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 classiication, when evaluated on a diverse database of over 1300 consumer images provided by Kodak.
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تاریخ انتشار 1998