TR#445: Indoor-Outdoor Image Classification

Martin Szummer and Rosalind W. Picard

IEEE International Workshop on Content-based Access of Image and Video Databases, Jan 1998
Bombay, India

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: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model parameters (3) 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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