Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network
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Research Article Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network Lalit Gupta, Vinod Pathangay, Arpita Patra, A. Dyana, and Sukhendu Das Visualization and Perception Laboratory, Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai-600 036, India Received 1 December 2005; Revised 22 May 2006; Accepted 27 May 2006 Recommended by Stefan Winkler 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. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Classification of a scene as belonging to indoor or outdoor is a challenging problem in the field of pattern recognition. This is due to the extreme variability of the scene content and the difficulty in explicitly modeling scenes with indoor and outdoor content. Such a classification has applications in content-based image and video retrieval from archives, robot navigation, large-scale scene content generation and representation, generic scene recognition, and so forth. Humans classify scenes based on certain local features along with the context or association with other features. This context is learned by experience (training). Some examples of such local features are the presence of trees, water bodies, exterior of buildings, sky in an outdoor scene and the presence of straight lines or regular flat-shaded objects or regions such as walls, windows, artificial man-made objects in an indoor scene. Also, the types of features that humans perceive from images are based on color, texture, and shape of local regions or image segments. In this work, we represent the image as a collection of segments that can be of arbitrary shape. From each segment color, texture, and shape features are extracted. Therefore, the problem of indoor versus outdoor scene classification is a feature set classification problem where the number of feature vectors in the feature set is not constant, as the number of segments in an image varies. Also, there is no implicit ordering of the feature vectors in the feature set. This
rules out the use of classifiers that take fixed dimension input feature vectors for classification. Hence we propose a modified probabilistic neural network that can handle variability in the feature
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