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Open Access

Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network

  • Lalit Gupta1Email author,
  • Vinod Pathangay1,
  • Arpita Patra1,
  • A. Dyana1 and
  • Sukhendu Das1
EURASIP Journal on Advances in Signal Processing20062007:094298

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

Received: 1 December 2005

Accepted: 27 May 2006

Published: 18 September 2006

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

ColorNeural NetworkInformation TechnologyFeature VectorQuantum Information

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Authors’ Affiliations

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

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Copyright

© Lalit Gupta et al. 2007

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