This article is part of the series Image Perception.

Open Access Research Article

Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network

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

Author Affiliations

Visualization and Perception Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India

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EURASIP Journal on Advances in Signal Processing 2007, 2007:094298 doi:10.1155/2007/94298


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2007/1/094298


Received:1 December 2005
Revisions received:22 May 2006
Accepted:27 May 2006
Published:18 September 2006

© 2007 Gupta et al.

We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy -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.

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