EURASIP Journal on Advances in Signal Processing 
Volume 2007 (2007), Article ID 94298, 10 pages
doi:10.1155/2007/94298
Research Article

Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network

Lalit Gupta, Vinod Pathangay, Arpita Patra, A. Dyana, and Sukhendu Das

 Visualization and Perception Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
Received 1 December 2005; Revised 22 May 2006; Accepted 27 May 2006

Recommended by Stefan Winkler

Abstract

We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy C-means clustering (FCM) and features based on color, texture, and shape are extracted from each of the image segments. The image is thus represented by a feature set, with a separate feature vector for each image segment. As the number of segments differs from one scene to another, the feature set representation of the scene is of varying dimension. Therefore a modified PNN is used for classifying the variable dimension feature sets. The proposed technique is evaluated on two databases: IITM-SCID2 (scene classification image database) and that used by Payne and Singh in 2005. The performance of different feature combinations is compared using the modified PNN.