Abstract
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.
Keywords
Autoregressive modelComputer scienceArtificial intelligenceHistogramDiscrete cosine transformPattern recognition (psychology)Feature vectorInvariant (physics)Feature (linguistics)Contextual image classificationImage (mathematics)Computer visionMathematicsStatistics
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 42-51
- Citations
- 638
- Access
- Closed
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Cite This
Martin Szummer,
Rosalind W. Picard
(2002).
Indoor-outdoor image classification.
, 42-51.
https://doi.org/10.1109/caivd.1998.646032
Identifiers
- DOI
- 10.1109/caivd.1998.646032