tailieunhanh - Báo cáo hóa học: " Research Article Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 94298 10 pages doi 2007 94298 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. 1. 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 .