Conference Paper

Constructing SURF visual-words for pornographic images detection

Lab. of Knowledge Process. & Networked Manuf., Hunan Univ. of Sci. & Technol., Xiangtan, China
DOI: 10.1109/ICCIT.2009.5407272 Conference: Computers and Information Technology, 2009. ICCIT '09. 12th International Conference on
Source: IEEE Xplore

ABSTRACT Pornographic images detection is necessary for us to filter out objectionable information on the Internet. Bag-of-visual-words (BoVW) based pornographic images detection is promising because it can compensate the defect of the traditional approach. However, there are many choices to construct visual-words which are crucial to the tradeoff between the speed and the performance. We propose a novel method of constructing SURF (speeded up robust features) visual-words in skin regions and combining it with color moments. The results show that the performance of SURF visual-words is better than that of SIFT (scale-invariant feature transform) visual-words and our method is more effective to detect pornographic images than many existing methods.

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    ABSTRACT: The pornographic images recognition can be seen as a special kind of object recognition task,but current pornographic images filtering algorithms using BoVF approaches have some problems,such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model using High-level Semantic features.Firstly,we optimize BoVW model to minimize semantic gap between low-level features and high-level semantic features and then high-level semantic dictionary is constructed by fusing the context of the visual vocabularies and spatial-related high- level semantic features of pornographic images. Experimental results show that the model has an accuracy up to 87.6% when testing the multi-person pornographic images, which is significantly higher than the existing pornographic images filtering algorithm based on Bag-Of-Visual-Words.
    Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011


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Jun 1, 2014