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Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

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posted on 2023-07-26, 14:55 authored by S. Hadi Yaghoubyan, Mohd Aizaini Maarof, Anazida Zainal, Mohd Foad Rohani, Mahdi Maktab Dar Oghaz
Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity.

History

Refereed

  • Yes

Volume

2

Issue number

6

Page range

27-33

Publication title

Journal of Soft Computing and Decision Support Systems

ISSN

2289-8603

Publisher

Penerbit UTM Press

File version

  • Published version

Language

  • eng

Legacy posted date

2020-03-10

Legacy creation date

2020-03-10

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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