<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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,...

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Online veröffentlicht: 30. Juni 2018

Seitenbereich: 89 - 97

Eingereicht: 15. Aug. 2017

Akzeptiert: 02. Apr. 2018

DOI: https://doi.org/10.2478/cait-2018-0030

Schlüsselwörter
Supervised learning, DCT, NSVC, shape recognition, SVM, feature extraction

© 2018 B. Nassih et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification

Online veröffentlicht: 30. Juni 2018

Seitenbereich: 89 - 97

Eingereicht: 15. Aug. 2017

Akzeptiert: 02. Apr. 2018

DOI: https://doi.org/10.2478/cait-2018-0030

SchlüsselwörterSupervised learning, DCT, NSVC, shape recognition, SVM, feature extraction

© 2018 B. Nassih et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Supervised learning, DCT, NSVC, shape recognition, SVM, feature extraction