The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Open set Object Detection combining Multi-branch Tree and ASSL

다중 분기 트리와 ASSL을 결합한 오픈 셋 물체 검출

  • Received : 2018.09.04
  • Accepted : 2018.10.05
  • Published : 2018.10.31
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Abstract

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

최근 많은 이미지 데이터 셋들은 일반적인 특성을 추출하기 위한 다양한 데이터 클래스와 특징을 가지고 있다. 하지만 이러한 다양한 데이터 클래스와 특징으로 인해 해당 데이터 셋으로 훈련된 물체 검출 딥러닝 모델은 데이터 특성이 다른 환경에서 좋은 성능을 내지 못하는 단점을 보인다. 이 논문에서는 하위 카테고리 기반 물체 검출 방법과 오픈셋 물체 검출 방법을 이용하여 이를 극복하고, 강인한 물체 검출 딥러닝 모델을 훈련하기 위해 능동 준지도 학습 (Active Semi-Supervised Learning)을 이용한 다중 분기 트리 구조를 제안한다. 우리는 이 구조를 이용함으로써 데이터 특성이 다른 환경에서 적응할 수 있는 모델을 가질 수 있고, 나아가 이 모델을 이용하여 이전의 모델보다 높은 성능을 확보 할 수 있다.

Keywords

References

  1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Adv. Neural Inf. Process. Syst., pp. 1-9, 2012. DOI: http:/doi.org/10.1109/5.726791
  2. O. Russakovsky et al., "ImageNet Large Scale Visual Recognition Challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, 2015. DOI: https://doi.org/10.1007/s11263-015-0816-y
  3. T. Y. Lin et al., "Microsoft COCO: Common objects in context," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8693 LNCS, no. PART 5, pp. 740-755. DOI: https://doi.org/10.1007/978-3-319-10602-1_48
  4. L. P. Jain, W. J. Scheirer, and T. E. Boult, "Multi-class open set recognition using probability of inclusion," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8691 LNCS, no. PART 3, pp. 393-409, 2014. DOI: https://doi.org/10.1007/978-3-319-10578-9_26
  5. K. Ding, C. Huo, Y. Xu, Z. Zhong, and C. Pan, "Sparse hierarchical clustering for VHR image change detection," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 577-581, 2015. DOI: https://doi.org/10.1109/LGRS.2014.2351807
  6. Y. Xiang, W. Choi, Y. Lin, and S. Savarese, "Subcategory-Aware convolutional neural networks for object proposals & detection," Proc. - 2017 IEEE Winter Conf. Appl. Comput. Vision, WACV 2017, pp. 924-933, 2017. DOI: https://doi.org/10.1109/WACV.2017.108
  7. J. Dai, S. Yan, X. Tang, and J. T. Kwok, "Locally adaptive classification piloted by uncertainty," in Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006, pp. 225-232. DOI: https://doi.org/10.1145/1143844.1143873
  8. K. He, X. Zhang, S. Ren, and J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 1904-1916, 2015. DOI: https://doi.org/10.1109/TPAMI.2015.2389824
  9. M. D. Zeiler and R. Fergus, "Visualizing and Understanding Convolutional Networks arXiv:1311.2901v3 [cs.CV] 28 Nov 2013," Comput. Vision-ECCV 2014, vol. 8689, pp. 818-833, 2014. DOI: https://doi.org/10.1007/978-3-319-10590-1_53
  10. C. Szegedy et al., "Going deeper with convolutions," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June- 2015, pp. 1-9, 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298594
  11. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," pp. 1-14, 2014. DOI: https://doi.org/ 10.1109/ACPR.2015.7486599
  12. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," 2015. DOI: https://doi.org/10.1109/CVPR.2016.90
  13. A. G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," 2017. DOI: arXiv:1704.04861
  14. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142-158, 2016. DOI: https://doi.org/10.1109/TPAMI.2015.2437384
  15. R. Girshick, "Fast R-CNN," in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 1440-1448. DOI: https://doi.org/10.1109/ICCV.2015.169
  16. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Nips, pp. 1-10, 2015. DOI: https://doi.org/10.1109/TPAMI.2016.2577031
  17. W. Liu et al., "SSD : Single Shot MultiBox Detector," pp. 1-15. DOI: https://arxiv.org/abs/1512.02325
  18. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2015. DOI: https://doi.org/10.1109/CVPR.2016.91
  19. J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," 2016. DOI: https://doi.org/10.1109/CVPR.2017.690
  20. J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," 2018. DOI: https://arxiv.org/abs/1804.02767
  21. T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," Proc. IEEE Int. Conf. Comput. Vis., vol. 2017- Octob, pp. 2999-3007, 2017. DOI: https://doi.org/10.1109/ICCV.2017.324
  22. J. Dong, Q. Chen, J. Feng, K. Jia, Z. Huang, and S. Yan, "Looking Inside Category: Subcategory- Aware Object Recognition," IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 8, pp. 1322- 1334, 2015. DOI: https://doi.org/10.1109/TCSVT.2014.2355697
  23. D. Roy, P. Panda, and K. Roy, "Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning," pp. 1-12, 2018. DOI: http://arxiv.org/abs/1802.05800
  24. Z. Yan et al., "HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition," 2014. DOI: https://doi.org/10.1109/ICCV.2015.314
  25. J. Fan et al., "HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition," IEEE Trans. Image Process., vol. 26, no. 4, pp. 1923-1938, 2017. DOI: https://doi.org/10.1109/TIP.2017.2667405
  26. J. Ye, J. Ni, and Y. Yi, "Deep Learning Hierarchical Representations for Image Steganalysis," IEEE Trans. Inf. Forensics Secur., vol. 12, no. 11, pp. 2545-2557, 2017. DOI: https://doi.org/10.1109/TIFS.2017.2710946
  27. C. Du, J. Zhu, and B. Zhang, "Learning Deep Generative Models with Doubly Stochastic MCMC," 2015. DOI: https://doi.org/10.1109/TNNLS.2017.2688499
  28. M. Khanum Tahira Mahboob Assistant Professor Assistant Professor and W. Imtiaz Humaraia Abdul Ghafoor Rabeea Sehar, "A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance," 2015. DOI: https://doi.org/10.5120/21131-4058
  29. X. Zhu, "Semi-Supervised Learning Literature Survey Contents," Sci. York, vol. 10, no. 1530, p. 10, 2008.
  30. B. Settles, "Active Learning Literature Survey," Mach. Learn., vol. 15, no. 2, pp. 201-221, 2010. https://doi.org/10.1007/BF00993277
  31. P. K. Rhee, E. Erdenee, S. D. Kyun, M. U. Ahmed, and S. Jin, "Active and semi-supervised learning for object detection with imperfect data," Cogn. Syst. Res., vol. 45, pp. 109-123, 2017. DOI: https://doi.org/10.1016/j.cogsys.2017.05.006
  32. P. Zhu, H. Wang, T. Bolukbasi, and V. Saligrama, "Zero-Shot Detection," 2018. DOI: http://arxiv.org/abs/1803.07113
  33. H. Chen, Y. Wang, G. Wang, and Y. Qiao, "LSTD: A Low-Shot Transfer Detector for Object Detection," 2018. DOI: https://arxiv.org/abs/1803.01529
  34. J. W. Kim, P. K. Rhee, "High Efficiency Adaptive Facial Expression Recognition based on Incremental Active Semi-Supervised Learning," The Journal of The Institute of Internet, Broadcasting and Communication(JIIBC), Vol. 17, No. 2, pp. 165-171, 2017. DOI: http://www.earticle.net/article.aspx?sn=300918 https://doi.org/10.7236/JIIBC.2017.17.2.165
  35. S. W. Jang, G. Lee, M. Jung, "Effective Detection of Target Region Using a Machine Learning Algorithm," Journal of the Korea Academia- Industrial cooperation Society, Vol. 19, No. 5, pp. 697-704, 2018. DOI: https://doi.org/10.5762/KAIS.2018.19.5.697
  36. S. Park, T. Jeon, S. Kim, S. Lee, J. Kim, "Deep learning based symbol recognition for the visually impaired," Journal of KIIT, Vol.9, No.3, pp. 249-256, 2016. DOI: http://dx.doi.org/10.17661/jkiiect.2016.9.3.249