Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

Blog Article

In this paper, a novel approach for identifying normal and obscene videos is proposed.In order to classify different episodes of a video independently and discard the need to process Dishwasher Drain Hose Holder all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames.In the second step, three different features including 1- structural features based on single frame information, 2- features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video.

The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features.For the final step, we size-matters-enlargers use fuzzy or a Weighted Support Vector Machine (WSVM) classifier to identify video episodes.We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos.

Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation.The proposed method is evaluated using 1100 videos in different environmental and illumination conditions.The experimental results show a correct recognition rate of 94.

2% for the proposed algorithm.