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Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network

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.

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Correspondence to Lalit Gupta.

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Gupta, L., Pathangay, V., Patra, A. et al. Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network. EURASIP J. Adv. Signal Process. 2007, 094298 (2006). https://doi.org/10.1155/2007/94298

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Keywords

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