Scene categorization to indoor vs outdoor may be approached by using low-level features for inferring high-level information about the image. Low-level features such as color and texture have been used extensively in image understanding research, however, they cannot solve the problem completely. We propose the use of a Bayesian network for integrating knowledge from low-level and semantic features for indoor vs outdoor classification of images. Using ground truth data for sky and grass detection, we demonstrate that the classification performance can be significantly improved when semantic features are employed in the classification process.

Publication Date



"Indoor vs outdoor classification of consumer photographs using low-level and semantic features," Proceedings of the 2001 International Conference on Image Processing, vol. 2. The Institute of Electrical and Electronics Engineers. Held in Thessaloniki, Greece: 7-10 October 2001. ©2001 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.ISBN:0-7803-6725-1Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus