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2002
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A Computationally Efficient Approach to Indoor/Outdoor Scene Classification

12 years 2 months ago
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
Prior research in scene classification has shown that high-level information can be inferred from low-level image features. Classification rates of roughly 90% have been reported using low-level features to predict indoor scenes vs. outdoor scenes. However, the high classification rates are often achieved by using computationally expensive, high-dimensional feature sets, thus limiting the practical implementation of such systems. We show that a more computationally efficient approach to indoor/outdoor classification can yield classification rates comparable to the best methods reported in the literature. A low complexity, low-dimensional feature set is used in conjunction with a two-stage Support Vector Machine classification scheme to achieve a classification rate of 90.2% on a large database of consumer photographs.
Navid Serrano, Andreas E. Savakis, Jiebo Luo
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2002
Where ICPR
Authors Navid Serrano, Andreas E. Savakis, Jiebo Luo
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