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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 730143, 9 pages
Research Article

Entropy-Based and Weighted Selective SIFT Clustering as an Energy Aware Framework for Supervised Visual Recognition of Man-Made Structures

Electrical and Computer Engineering Department, The American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon

Received 14 July 2013; Accepted 30 September 2013

Academic Editor: Yudong Zhang

Copyright © 2013 Ayman El Mobacher et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1% on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand.