Research Article Open Access

OBJECTIONABLE IMAGE DETECTION IN CLOUD COMPUTING PARADIGM-A REVIEW

Rashed Mustafa1 and Dingju Zhu2
  • 1 , China
  • 2 University of Chinese Academy of Sciences, China
Journal of Computer Science
Volume 9 No. 12, 2013, 1715-1721

DOI: https://doi.org/10.3844/jcssp.2013.1715.1721

Submitted On: 8 October 2013 Published On: 25 November 2013

How to Cite: Mustafa, R. & Zhu, D. (2013). OBJECTIONABLE IMAGE DETECTION IN CLOUD COMPUTING PARADIGM-A REVIEW. Journal of Computer Science, 9(12), 1715-1721. https://doi.org/10.3844/jcssp.2013.1715.1721

Abstract

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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Keywords

  • Content Based Pornography Detection (CBPD)
  • Content Based Image Processing (CBIP)
  • Transfer Learning (TL)
  • Multimedia Cloud Computing (MCC)