Automatic Scene Classification:
Algorithms and Applications
by
Andreas Savakis, Ph.D.
Associate Professor and Department Head
Department of Computer Engineering
Rochester Institute of Technology
Automatic scene classification is an image understanding problem that has
applications in image database indexing and retrieval, selective image
enhancement and multimedia applications, such as automatic albuming.
In this talk, a computationally efficient approach to scene classification is
presented, where low level and semantic features are combined for improved
performance. The first classification stage is based on low level features and k-nearest
neighbor or Support Vector Machine classifiers. Low level features include
color histograms and wavelet texture features, while semantic features include
sky and grass information about the image. Knowledge integration of low level
semantic features is achieved via a Bayesian network during a second stage of
processing. Results are presented for a database of 1300 photographic
images and the overall classification performance is over 90 percent. Future
work aims at employing similar algorithms and high performance environments for
other computationally intensive image and video understanding tasks.