S. Daniel Madan Raja et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3033-3037 Wavelet Features Based War Scene Classification using Artificial Neural Networks S.Daniel Madan Raja* Senior Lecturer, Department of Information Technology Bannari Amman Institute of Technology, Sathyamangalam, India Dr.A.Shanmugam Principal Bannari Amman Institute of Technology Sathyamangalam, India Abstract This paper addresses the problem of war scene classification. Scene classification underlies many problems in visual perception such as object recognition and environment navigation. Scene classification, the classification of images into semantic categories (e.g. opencountry, mountains, highways and streets) is a challenging and important task nowadays. In this paper we are trying to classify the war scene category from the natural scene category. For this purpose two set of image categories were taken i.e., opencountry & war tank. By using Haar and Daubechies(db4) wavelets the features are extracted from the images. The extracted features are trained and tested with the help of feed forward back propagation algorithm using Artificial neural Networks. The complete work is experimented in Matlab 7.6.0 using real world dataset. Keywords: scene classification; haar and Daubechies wavelet; back propagation; artificial neural networks I. INTRODUCTION Scene and object classification is an important research topic in robotics and computer vision. Number of research problems have been studied and reported by the research community. Scene classification is a term that is usually used to classify the images into semantic categories (e.g. street, bedroom, mountain, or coast) [1], [2], [3, [4]. Classification is one of several primary categories of machine learning problems [5]. For the indoor-outdoor scene retrieval problem, the authors addressed how high-level scene properties can be inferred from classification of low-level image features [6]. In paper[7], authors propose an automated method based on the boosting algorithm to estimate image orientations. In [8], Bosch et al. present a scene description and segmentation system capable of recognizing natural objects (e.g., sky, trees, grass) under different outdoor conditions. In paper [9], the authors propose a new technique for the classification of indoor and outdoor images based on edge analysis. Analysis of texture [10] requires the identification of proper attributes or features that differentiate the textures of the image. Authors [11][12] analyze the efficiency of commonly used feature extraction methods such as haar features, invariant moments and co-occurrence matrix by using Artificial neural networks and support vector machines classifiers. This paper presents the war scene classification using Haar and Debauchies wavelet feature extraction methods using Artificial neural networks with feed forward back propagation algorithm. The organization of the paper is as follows: Section 2 describes Haar and Debauchies wavelet Features, Section 3 describes Artificial Neural Networks, Section 4 explains the proposed work, Section 5 deals with implementation of ANN, Section 6 deals with discussion, and finally Section 7 concludes with conclusion. II. HAAR AND DAUBECHIES WAVELET FEATURES Haar [11] and Daubechies [13] wavelets are widely used techniques for feature extraction, which are single-level one- dimensional wavelet decomposition and gives both an approximation and detailed coefficients. Approximation coefficients which are of size 128x1 for Haar wavelet and 131x1 for Daubechies wavelet are considered as the feature set for our problem domain. Pictorial representation of approximation coefficients and detailed coefficients of the above mentioned wavelets are shown in the Fig. 2.1. Hence, features F1 to F128 and F1 to F131 are considered as feature sets for Haar and Daubechies wavelets respectively. *corresponding author ISSN : 0975-3397 3033