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DESIGNING EFFICIENT MULTIMODAL CLASSIFICATION SYSTEMS BASED ON FEATURES AND SVM KERNELS SELECTION
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Journal Article
DESIGNING EFFICIENT MULTIMODAL CLASSIFICATION SYSTEMS BASED ON FEATURES AND SVM KERNELS SELECTION
2016
Overview
An efficient classification system uses only the most representative features extracted from images in order to reach a decision. A multimodal system may consider multiple sources of such information. Selecting those features is not a simple task due to the fact that multiple features selection (FS) methods exists, with multiple setup possibilities and multiple possible feature vectors to be applied on. Moreover, by applying the FS, the new vector may comprise too few features and the recognition accuracy to significantly drop. This paper proposes solutions to compensate that accuracy loss by the SVM kernel selection.
Publisher
Universitatea Tehnica Cluj-Napoca
Subject
