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Texture based Scene Categorization using Artificial Neural Networks and Support Vector Machines: A Comparative Study Devendran V1, Hemalatha Thiagarajan2, Amitabh Wahi3 1Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, TamilNadu, India 2Department of Mathematics, National Institute of Technology, Trichy, TamilNadu, India 3Department of Information Technology, Bannari Amman Institute of Technology, TamilNadu, IndiaCategorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper uses gray level cooccurrence matrix method to extract features from the scenes and trying to recognize the scene categories called ‘MIT-street’ and ‘MIT-highways’. Artificial neural networks and support vector machines classifiers are used for the classification. The comparative results are proving efficiency of support vector machines towards scene categorization problems. The sample images are taken from the real world dataset. Keywords: Artificial Neural Networks, Gray Level Cooccurrence Matrix, Scene Categorization, Support Vector Machine.
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BibTex: @ARTICLE{P1150812008, AUTHOR = {Devendran V and Hemalatha Thiagarajan and Amitabh Wahi}, TITLE = {Texture based Scene Categorization using Artificial Neural Networks and Support Vector Machines: A Comparative Study}, JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing, GVIP}, YEAR = {2008},
VOLUME = {08}, ISSUE ={IV}, PAGES={45--52} }
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P1150812008
