Prior research in scene classification has focused on mapping a set of classic low-level vision features to semantically meaningful categories using a classifier engine. In this paper, we propose improving the established paradigm by using a simplified low-level feature set to predict multiple semantic scene attributes that are integrated probabilistically to obtain a final indoor/outdoor scene classification. An initial indoor/outdoor prediction is obtained by classifying computationally efficient, low-dimensional color and wavelet texture features using support vector machines. Similar low-level features can also be used to explicitly predict the presence of semantic features including grass and sky. The semantic scene attributes are then integrated using a Bayesian network designed for improved indoor/outdoor scene classification.

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Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

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Department, Program, or Center

Computer Engineering (KGCOE)


RIT – Main Campus