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2005
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A Two Level Approach for Scene Recognition

11 years 1 months ago
A Two Level Approach for Scene Recognition
Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-indoor) and multiple category of scene classification. We first learn mixture models for 20 basic classes of local image content based on color and texture information. Once trained, these models are applied to a test image, and produce 20 probability density response maps (PDRM) indicating the likelihood that each image region was produced by each class. We then extract some very simple features from those PDRMs, and use them to train a bagged LDA classifier for 10 scene categories. For this process, no explicit region segmentation or spatial context model are computed. To test this classification system, we created a labeled database of 1500 photos taken under very different environment and lighting conditions, using different cameras, and from 43 persons over 5 years. The classification rate of outdoor-indoo...
Le Lu, Kentaro Toyama, Gregory D. Hager
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Le Lu, Kentaro Toyama, Gregory D. Hager
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