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  • Research Article
  • Open Access

Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network

  • 1Email author,
  • 1,
  • 1,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20062007:094298

https://doi.org/10.1155/2007/94298

  • Received: 1 December 2005
  • Accepted: 27 May 2006
  • Published:

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 -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.

Keywords

  • Color
  • Neural Network
  • Information Technology
  • Feature Vector
  • Quantum Information

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Authors’ Affiliations

(1)
Visualization and Perception Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India

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Copyright

© Lalit Gupta et al. 2007

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