Abstract

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.

Publication Date

2002

Comments

"A computationally efficient approach to indoor/outdoor scene classification," Proceedings of the 16th International Conference on Pattern Recognition, vol. 4. Institute of Electrical and Electronics Engineers. Held in Quebec City, Canada: August 2002. ©2002 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISSN:1051-4651 Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Article

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Campus

RIT – Main Campus

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