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文章基本信息

  • 标题:Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network
  • 本地全文:下载
  • 作者:Lalit Gupta ; Vinod Pathangay ; Arpita Patra
  • 期刊名称:EURASIP Journal on Advances in Signal Processing
  • 印刷版ISSN:1687-6172
  • 电子版ISSN:1687-6180
  • 出版年度:2007
  • 卷号:2007
  • DOI:10.1155/2007/94298
  • 出版社:Hindawi Publishing Corporation
  • 摘要:

    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.

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