A Novel Scheme for Intelligent Recognition of Pornographic Images
This addresses the need for fast and reliable filtering of obscene content on the internet, but it appears incremental as it builds upon existing methods with new features and fusion techniques.
The paper tackles the problem of detecting pornographic images by introducing a new approach that combines two new features with traditional ones and uses fuzzy integral-based information fusion to merge MLP and Neuro-Fuzzy outputs, achieving a precision of 93% true positive and 8% false positive on training data and 87% and 5.5% on test data.
Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works.