文章基本信息

标题：New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification
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作者：B. Nassih ; M. Ngadi ; A. Amine 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2018
卷号：18
期号：2
页码：89-97
DOI：10.2478/cait-2018-0030
出版社：Bulgarian Academy of Science
摘要：Feature extraction is an interactive and iterative analysis process of a large dataset of raw data in order to extract meaningful knowledge. In this article, we present a strong descriptor based on the Discrete Cosine Transform (DCT), we show that the new DCT-based Neighboring Support Vector Classifier (DCT-NSVC) provides a better results compared to other algorithms for supervised classification. Experiments on our real dataset named BOSS, show that the accuracy of classification has reached 99%. The application of DCT-NSVC on MIT-CBCL dataset confirms the performance of the proposed approach.
关键词：Supervised learning; DCT; NSVC; shape recognition; SVM; feature; extraction