Feature Selection for Scene Categorization Using Support Vector Machines

Categorization 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 is classifying the scenes using support vector machine with radial basis kernel with p1=5. This work is double folded as to classify the scenes using support vector machine and to find better feature extraction method among the ones which have been used by the research community often i.e., wavelet features, invariant moments and co-occurrences matrix. The sample images are taken from the real world dataset.