Zernike based scene categorization using artificial neural networks

Natural scene categorization is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Thousands of images are generated every day, which implies the necessity to classify, organize and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. Many different approaches concerning scene classification have been proposed in the last few years. This paper presents a different approach to classify dasiaMIT-Streetpsila and dasiaMIT-highwaypsila scene categories using zernike moments and artificial neural networks. Back propagation is used for the paradigm of artificial neural networks. This work is implemented using various blocking models in the images. The comparative results are proving the importance of blocking and the efficiency of classifier towards scene classification problems. This complete work is carried out using real world data set.