Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Context Aware Feature Selection Model for Salient Feature Detection from Mobile Video Devices

모바일 비디오기기 위에서의 중요한 객체탐색을 위한 문맥인식 특성벡터 선택 모델

  • Lee, Jaeho (Department of Computer Education, Gyeongin National University of Education) ;
  • Shin, Hyunkyung (Department of Mathematical Science, Gachon University)
  • Received : 2014.12.01
  • Accepted : 2014.12.06
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

모바일 기기를 사용한 실시간 비디오 영상처리분야의 중요 객체탐색 및 추적의 문제에 있어서 난제는 복잡한 배경속에서 전경을 구분해 내는 일이다. 본 논문에서는 기계학습을 위한 특성벡터 선정의 문제를 위한 문맥인식 모델을 제시하여 잡음제거를 위한 기계학습기반의 구분자를 구현하였다. 수학적으로 NP-hard로 알려진 가장 가까운 이웃을 사용한 문맥인식 특성벡터 선정 알고리즘의 구현에 있어서, 본 논문은 연산횟수를 줄인 유사방법론에 대해 자세히 거론하였다. 또한, 문맥인식 성격을 가미한 특성벡터 선정을 통해 얻어진 특성 공간에서의 향상된 분리성에 대해 주성분 분석을 통해 엄밀한 분석결과를 제시하였다. 전반적인 성능 향상의 정도를 계측하기 위해 다양한 기계학습 방법론, 예를 들어, 다층신경망, 지원벡터기계, 나이브베이지안, 회귀분석 등을 사용해 비교결과를 제시하였다. 본 논문에서 제시한 방법론의 성능과 계산상 자원사용에 대한 내용을 결론으로 서술하였다.

Keywords

References

  1. B. Chen, H.H. Cheng, and J. Palen, "Integrating Mobile Agent Technology with Multi-Agent Systems for Distributed Traffic Detection and Management System," Transportation Research Part C, 17, 2009, pp. 1-10, 200 https://doi.org/10.1016/j.trc.2008.04.003
  2. D.M. Gavrila, "The Visual Analysis of Human Movement: A Survey," Computer Vision and Image Understanding, Vol. 73, No. 1, pp. 82-98, 1999. https://doi.org/10.1006/cviu.1998.0716
  3. B. Epshtein, E. Ofek, and Y. Wexler, "Detecting text in natural scenes with stroke width transform," in CVPR, pp. 2963 -2970, 2010.
  4. W. Boussellaa, A. Zahour, A. Alimi, "A methodology for the separation of foreground / background in Arabic historical manuscripts using hybrid methods," Proceeding SAC '07, pp. 605 - 609, 2007.
  5. K. Jung, K. I. Kim, and A. K. Jain, "Text information extraction in images and video: a survey," Pattern Recognition, vol. 37, no. 5, pp. 977 - 997, 2004. https://doi.org/10.1016/j.patcog.2003.10.012
  6. L. Zhang, T.K. Marks, M.H. Tong, H. Shan, and G.W. Cottrell, "SUN: a Bayesian framework for saliency using natural statistics", Journal of Vision, 8(7):32, 1-20, 2008.
  7. D. Gao, V. Mahadevan, and N. Vasconcelos. "The discriminant center-surround hypothesis for bottom-up saliency," in Proc. of NIPS, pp. 497-504, 2007.
  8. J. Ohya, A. Shio and S. Aksmatsu, "Recognizing characters in scene images," IEEE. Trans. PAMI, vol. 16, pp. 214-224, 1994. https://doi.org/10.1109/34.273729
  9. J.C. Shim, C. Dorai, and R., Bolle, "Automatic text extraction from video for content-based annotationand retrieval," Proc. 14th Int. Conf. on PR, vol. 1:16-20 pp. 618-620, 1998.
  10. R. Lienhart and W. Effelsberg, "Automatic Text Segmentation and Text Recognition for Video Indexing," TR-98-009, PraktischeInformatik IV, University of Mannheim, 1998.
  11. B.T. Chun, Y. Bae, and T.Y. Kim, "Automatic Text Extraction in Digital Videos Using FFT and Neural Network," Proc. IEEE Int. Fuzzy Sys. Conf. Seoul, Korea, 2:1112-1115, 1999.
  12. Qian, X., Liu, G., "Text Detection, Localization and Segmentation in Compressed Videos," Proc. Int. Conf. on Acoustics, Speech and Signal Processing, 2:385-388, 2006.
  13. D. Chen, K. Shearer, and H. Bourlard, "Text Enhancement with Symmetric Alter for Video OCR," Proc. Int. Conf. on Image Analysis and Recognition, pp.192- 197, 2001.
  14. W. Mao, F. Chung, K. Lanm, and W. Siu, "Hybrid Chinese/ English Text Detection in Images and Video Frames," Proc. Int. Conf. on CVPR, 3:1015-1018, 2002.
  15. P. Clark and M. Mirmehdi, "Finding Text Regions Using Localized Measures," Proc. 11th British Machine Vision Conference, pp.675-684, 2000.
  16. K.I. Kim, K. Jung, and J.H. Kim, "Texture-based approach for text detection in images using support vector machine sand continuously adaptive mean shift algorithm," IEEE Trans. PAMI, 25(12):1631-1639, 2003. https://doi.org/10.1109/TPAMI.2003.1251157
  17. J. Weinman, A. Hanson, and A. McCallum, "Sign Detection In Natural Images With Conditional Random Fields," IEEE Int. Work. on Machine Learning for Signal Processing, Brazil, Sep. 2004.
  18. S. Messelodi and C.M. Modena, "Automatic Identification and Skew Estimation of Text Lines in Real Scene Images," Pattern Recognition, 32 (1992) 791-810.
  19. M.A. Smith and T. Kanade, "Video Skimming for Quick Browsing Based on Audio and Image Characterization", Carnegie Mellon University, Technical Report CMU-CS-95-186, July 1995.
  20. Y.M.Y. Hasan and L.J. Karam, "Morphological Text Extraction from Images," IEEE Transactions on Image Processing, 9 (11) pp. 1978-1983, 2000. https://doi.org/10.1109/83.877220
  21. D. Chen, K. Shearer, and H. Bourlard, "Text Enhancement with Asymmetric Filter for Video OCR," Proc. of Int. Conf. on Image Analysis and Processing, pp. 192-197, 2001.
  22. Wang, K.Q., Kangas, J.A., "Character location in scene images from digital camera," Pattern Recognition, 36(10): pp. 2287-2299, 2003. https://doi.org/10.1016/S0031-3203(03)00082-7
  23. D.Q. Zhang and F.H. Chang, "Learning to Detect Scene Text Using a Higher-Order MRF with Belief Propagation," Proc. Int. Conf. on CVPR, p.101-107, 2004.
  24. K.K. Kim and Y.K. Chung, "Scene Text Extraction in Natural Scene Images Using Hierarchical Feature Combining and Verification," Proc. Int. Conf. on CVPR, 2: pp. 679-682, 2004.
  25. C. Liu, C. Wang, and R. Dai, "Text Detection in Images Based on Unsupervised Classification of Edge-based Features," ICDAR, 2005.
  26. M.R. Lyu, J. Song, and M. Cai, "A comprehensive method for multilingual video text detection, localization, and extraction," IEEE Trans. Circuits Syst. Video Technol.,15(2):243-255, 2005. https://doi.org/10.1109/TCSVT.2004.841653
  27. H. Takahashi and M. Nakajima, "Region Graph Based Text Extraction from Outdoor Images," Proc. 3rd Int. Conf. on Info. Tech. and App., 1:680-685, 2005.
  28. Zhang, Statman, and Shasha, "On the editing distance between unordered labeled trees," Information Processing Letters, 42:133-139, 1992. https://doi.org/10.1016/0020-0190(92)90136-J
  29. Y. Zhong, K. Karu, and A.K. Jain, "Locating text in complex images," Pattern Recognition, 28(10), 1523-1535, 1995. https://doi.org/10.1016/0031-3203(95)00030-4
  30. V. Wu, R. Manmatha, and E.M. Riseman, "TextFinder: An Automatic System to Detect and Recognize Text in Images," IEEE Trans. on PAMI, 21(11), pp. 1224-1229, 1999. https://doi.org/10.1109/34.809116
  31. B. Sin, S. Kim, and B. Cho, "Locating Characters in Scene Images using Frequency Features," Proc. of Int. Conf. on Pattern Recognition, 2002.
  32. W. Mao, F. Chung, K. Lanm, and W. Siu, "Hybrid Chinese/English Text Detection Technology on Image Sequence", Proc. of Int. Conf. on Pattern Recognition, vol. 3, pp. 1015-1018, 2002.
  33. D. Arthur and S. Vassilvitskii. "k-means++: the advantages of careful seeding," In Proc. of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027, 2007.
  34. T Plotz , NY. Hammerla and POlivier, "Feature learning for activity recognition in ubiquitous computing", Pro of the Twenty-Second international joint Conf. on Artificial Intelligence, pp. 1729-1734, 2011