Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network. (18th September 2006)

• Record Type: Journal Article
• Authors: Gupta Gupta, Lalit Lalit; Pathangay Pathangay, Vinod Vinod; Patra Patra, Arpita Arpita; Dyana Dyana, A. A.; Das Das, Sukhendu Sukhendu
• Other Names: Winkler Winkler Stefan Stefan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-09-18
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/94298
• Languages: English
• ISSNs: 1687-6172

Abstract

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