期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：In this paper the support vector machine is utilized to recognize the object from the given image. The proposed method for object recognition is associated with the reduction of feature vector by Kernel Principal Component Analysis (KPCA) and recognition using the Support Vector Machine (SVM) classifier. Also in this paper the feature extraction method extracts features from global descriptors of the image. In the feature extraction process for an image, global features are extracted and formed as feature vector. For the entire training image the feature vector is generated and dimension reduction is done using KPCA. The reduced feature vector is used to train the SVM classifier. Later test images are given as input and tested the performance of the Classifier. To prove the efficiency of the SVM Classifier, Back Propagation Neural Network is used for the object recognition. From the comparison, SVM classifier outperforms.
关键词：Support Vector Machine; Object Recognition; Moment Inavariants; Kernel Principal Component Analysis
