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Journal of Sensors
Volume 2018, Article ID 7497243, 12 pages
https://doi.org/10.1155/2018/7497243
Research Article

Harmful Content Detection Based on Cascaded Adaptive Boosting

1Department of Software, Anyang University, No. 22, 37-Beongil, Samdeok-Ro, Manan-Gu, Anyang 430-714, Republic of Korea
2Department of Computer Engineering, Anyang University, No. 22, 37-Beongil, Samdeok-Ro, Manan-Gu, Anyang 430-714, Republic of Korea

Correspondence should be addressed to Sang-Hong Lee; moc.liamg@asodeelhs

Received 1 March 2018; Revised 24 July 2018; Accepted 15 August 2018; Published 21 October 2018

Academic Editor: Sandra Sendra

Copyright © 2018 Seok-Woo Jang and Sang-Hong Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. AlSkaif, B. Bellalta, M. G. Zapata, and J. M. Barcelo Ordinas, “Energy efficiency of MAC protocols in low data rate wireless multimedia sensor networks: a comparative study,” Ad Hoc Networks, vol. 56, pp. 141–157, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Yousafzai, V. Chang, A. Gani, and R. M. Noor, “Multimedia augmented M-learning: issues, trends and open challenges,” International Journal of Information Management, vol. 36, no. 5, pp. 784–792, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Gao, Y. Wen, H. Zhao, and Y. Meng, “Secure data aggregation in wireless multimedia sensor networks based on similarity matching,” International Journal of Distributed Sensor Networks, vol. 2014, Article ID 494853, 6 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. D. Du, B. Qi, M. Fei, and Z. Wang, “Quantized control of distributed event-triggered networked control systems with hybrid wired-wireless networks communication constraints,” Information Sciences, vol. 380, pp. 74–91, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. X. L. Liu, W. Hu, C. Luo, and F. Wu, “Compressive image broadcasting in MIMO systems with receiver antenna heterogeneity,” Signal Processing: Image Communication, vol. 29, no. 3, pp. 361–374, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Kim, N. Kim, D. Lee, S. Park, and S. Lee, “Watermarking two dimensional data object identifier for authenticated distribution of digital multimedia contents,” Signal Processing: Image Communication, vol. 25, no. 8, pp. 559–576, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Yang, H. Wang, Z. Lv et al., “Multimedia recommendation and transmission system based on cloud platform,” Future Generation Computer Systems, vol. 70, pp. 94–103, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Huang, L. Xu, Q. Duan, C.-C. Xing, J. Luo, and S. Yu, “Modeling and performance analysis for multimedia data flows scheduling in software defined networks,” Journal of Network and Computer Applications, vol. 83, pp. 89–100, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. Y.-J. Park, S.-H. Weon, J.-K. Sung, H.-I. Choi, and G.-Y. Kim, “Identification of adult images through detection of the breast contour and nipple,” Information - An International Interdisciplinary Journal, vol. 15, no. 7, pp. 2643–2652, 2012. View at Google Scholar
  10. J.-S. Lee, Y.-M. Kuo, P.-C. Chung, and E.-L. Chen, “Naked image detection based on adaptive and extensible skin color model,” Pattern Recognition, vol. 40, no. 8, pp. 2261–2270, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. S.-W. Jang, Y.-J. Park, G.-Y. Kim, H.-I. Choi, and M.-C. Hong, “An adult image identification system based on robust skin segmentation,” Journal of Imaging Science and Technology, vol. 55, no. 2, pp. 020508–020508-10, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J.-S. Yoon, G.-Y. Kim, and H.-I. Choi, “Development of an adult image classifier using skin color,” The Journal of the Korea Contents Association, vol. 9, no. 4, pp. 1–11, 2009. View at Publisher · View at Google Scholar
  13. J.-Y. Park, S.-S. Park, Y.-G. Shin, and D.-S. Jang, “A novel system for detecting adult images on the internet,” KSII Transactions on Internet and Information Systems, vol. 4, no. 5, pp. 910–924, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. J.-L. Shih, C.-H. Lee, and C.-S. Yang, “An adult image identification system employing image retrieval technique,” Pattern Recognition Letters, vol. 28, no. 16, pp. 2367–2374, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. S.-W. Jang and G.-Y. Kim, “Learning-based detection of harmful data in mobile devices,” Mobile Information Systems, vol. 2016, Article ID 3919134, 8 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. S.-I. Joo, S.-W. Jang, S.-W. Han, and G.-Y. Kim, “ASM-based objectionable image detection in social network services,” International Journal of Distributed Sensor Networks, vol. 10, Article ID 673721, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696–706, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. K.-M. Lee, “Component-based face detection and verification,” Pattern Recognition Letters, vol. 29, no. 3, pp. 200–214, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar
  20. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–165, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Feng, H. Zhao, X. Li, X. Zhang, and H. Li, “A multi-scale 3D Otsu thresholding algorithm for medical image segmentation,” Digital Signal Processing, vol. 60, pp. 186–199, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. L. He, X. Zhao, Y. Chao, and K. Suzuki, “Configuration-transition-based connected component labeling,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 943–951, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Ma, X.-P. Zhang, J. Si, and G. P. Abousleman, “Bidirectional labeling and registration scheme for grayscale image segmentation,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2073–2081, 2005. View at Google Scholar
  24. S. Lou, X. Jiang, and P. J. Scott, “Algorithms for morphological profile filters and their comparison,” Precision Engineering, vol. 36, no. 3, pp. 414–423, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Su, C. Sun, C. Zhang, and T. D. Pham, “A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information,” Pattern Recognition, vol. 47, no. 10, pp. 3193–3208, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. E. Hamuda, B. Mc Ginley, M. Glavin, and E. Jones, “Automatic crop detection under field conditions using the HSV colour space and morphological operations,” Computers and Electronics in Agriculture, vol. 133, pp. 97–107, 2017. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Mohamed, A. Issam, B. Mohamed, and B. Abdellatif, “Real-time detection of vehicles using the Haar-like features and artificial neuron networks,” Procedia Computer Science, vol. 73, pp. 24–31, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Mohamed, A. Issam, A. Mohamed, and B. Abdellatif, “ECG image classification in real time based on the Haar-like features and artificial neural networks,” Procedia Computer Science, vol. 73, pp. 32–39, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. X. Zhao, X. Chai, Z. Niu, C. Heng, and S. Shan, “Context modeling for facial landmark detection based on non-adjacent rectangle (NAR) Haar-like feature,” Image and Vision Computing, vol. 30, no. 3, pp. 136–146, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Chai and Y. Wang, “Face detection based on extended Haar-like features,” in 2010 2nd International Conference on Mechanical and Electronics Engineering, pp. 442–445, Kyoto, Japan, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Sun, H. Fujita, P. Chen, and H. Li, “Dynamic financial distress prediction with concept drift based on time weighting combined with AdaBoost support vector machine ensemble,” Knowledge-Based Systems, vol. 120, pp. 4–14, 2017. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Sun, S. Chen, J. Wang, and H. Chen, “A robust multi-class AdaBoost algorithm for mislabeled noisy data,” Knowledge-Based Systems, vol. 102, pp. 87–102, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Gao, P. Li, Y. Zhang, J. Liu, and L. Wang, “People counting based on head detection combining AdaBoost and CNN in crowded surveillance environment,” Neurocomputing, vol. 208, pp. 108–116, 2016. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Dogan and O. Akay, “Using AdaBoost classifiers in a hierarchical framework for classifying surface images of marble slabs,” Expert Systems with Applications, vol. 37, no. 12, pp. 8814–8821, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Wang, Z. Qin, Z. Jin, and S. Zhang, “Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning,” Journal of Systems and Software, vol. 83, no. 7, pp. 1137–1147, 2010. View at Publisher · View at Google Scholar · View at Scopus