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Comparison of Performance between Different SVM Kernels for the Identification of Adult Video

Authors: Driss Aboutajdine, Hajar Bouirouga, Sanaa El Fkihi, Abdeilah Jilbab


In this paper we propose a method for recognition of adult video based on support vector machine (SVM). Different kernel features are proposed to classify adult videos. SVM has an advantage that it is insensitive to the relative number of training example in positive (adult video) and negative (non adult video) classes. This advantage is illustrated by comparing performance between different SVM kernels for the identification of adult video.

Keywords: classification, Feature Extraction, support vector machine, skin detection, Pornographic videos, Video filtering

Digital Object Identifier (DOI):

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