Affiliation:
1. Department of Information & Technology, Malaysia University of Science & Technology, Petaling Jaya Selangor Kota Damansara, Selangor 47810, Malaysia
Abstract
The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.
Publisher
World Scientific Pub Co Pte Lt
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition
Reference18 articles.
1. V. Vapnik, Statistical Learning Theory, 1st edn. (John-Wiley, New York, 1998), pp. 434–437.
2. Integrating Sparse and Collaborative Representation Classifications for Image Classification
Cited by
3 articles.
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