OBJECTIONABLE IMAGE DETECTION IN CLOUD COMPUTING PARADIGM-A REVIEW
- 1 , China
- 2 University of Chinese Academy of Sciences, China
Copyright: © 2020 Rashed Mustafa and Dingju Zhu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Obscenity detection from images and videos are now crucial due to social and ethical reasons. It has been two decades the research on this field started. Most of the works are based on skin color detection, which are not suitable for finding obscenity. The reason for this is that, there are many skins like objects such as beach photos, human skin like animal’s fur, skin colored painting that enables false positive and negative rate. In addition all works performed well on some particular set of images or video data. In this research some aspects of obscenity detection is described delineating strength, weakness and possible extensions of prior works. Introducing some new features and incorporation of multiple classifiers and transfer learning will lead the work more robust. Moreover, traditional multimedia cloud computing has been investigated in this study and proposed some new research ideas.
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- Content Based Pornography Detection (CBPD)
- Content Based Image Processing (CBIP)
- Transfer Learning (TL)
- Multimedia Cloud Computing (MCC)