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ORB feature based web pornographic image recognition
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DOI：10.1016/j.neucom.2015.06.055.png)
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Abstract
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Taken the requirements of web pornographic image recognition both on precision and speed, a pornographic image recognition method based on Oriented FAST and Rotated BRIEF (ORB) is proposed in this paper. The whole recognition process can be divided into two parts: coarse detection and fine detection. Coarse detection can identify the non-pornographic images with no or fewer skin-color regions and facial images quickly. For the remaining images containing much more skin-color regions, fine detection is conducted, which includes three steps: (1) extract ORB descriptors from the skin-color regions and represent the descriptors based on Bag of Words (BOW) model, (2) construct the feature vector combining ORB feature with 72-dimensional Hue, Saturation, Value (HSV) color feature of the whole image, (3) train the classification model using Support Vector Machine (SVM) and apply it for image recognition. The experimental results show that the proposed method can obtain better recognition precision and drastically reduce the average time cost to 1/4 of the method based on Scale Invariant Feature Transform (SIFT). (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Pornographic image recognition
ORB
SIFT
Visual words
Feature vector
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