Scene Classification Based On SIFT And SVM

Scene classification is a foundational process in computer vision, and it plays an important role in pattern recognition, machine learning, image content understanding, image retrieve. Classical SIFT feature is an invariant feature to rotation, translation in the field of feature extraction. SIFT Feature is provided with good remarkable and robust. SVM is one of newly and effective classifiers. SVM is based on little sample Statistical Learning Theory and independent on designer's experience. So it avoids empirical in Artificial Neural Networks. SVM is changed to convex optimize, so it guarantees global optimal. This paper presents SVM scene classification method based on SIFT feature.The main content of this paper includes:first, introduce the current research situation of scene classification, SIFT feature, SVM. Then, based on SIFT feature and SVM, we proposed the theory framework of scene classification. Specific work includes:Firstly, we preprocess the scene images before classification, such as convert the color images to gray images, and scale the images to the same size; Secondly, we present the scene classification algorithm based on SIFT and SVM, SIFT feature points are extracted, they are vectors, and then train SVM by those vectors, At last, recognition work is completed using SIFT feature points and SVM. Our experiment result shows that our algorithm performs better than the one based on geodesic distance.