대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

해마신경망을 이용한 관심 객체 기반의 효율적인 멀티미디어 검색 시스템의 개발

The Development of Efficient Multimedia Retrieval System of the Object-Based using the Hippocampal Neural Network

  • 발행 : 2006.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 해마신경망(HCNN:HippoCampal Neural Network)을 이용하여 사용자 친화적인 객체 기반 멀티미디어 검색시스템을 제안한다. 내용 기반 검색(Content-based Retrieval)에 관한 대부분의 기존의 질의 방법은 입력 영상에 의한 질의 또는 컬러(color), 형태(shape), 질감(texture)등과 같은 low-level의 특징을 사용한다. 본 논문에서 제안하는 방법은 MPEG 기반의 압축 비디오 스트림으로부터 장면 전환 검출을 수행하여 샷을 검출한다. 이 샷 프레임에서 컬러 객체의 자동 추출을 위하여 similar colorization과 ACE(Adaptive Circular filter and Edge) 알고리즘을 사용한다. 그리고 이렇게 추출된 특징을 해마 신경망을 통하여 학습한 후 멀티미디어 검색 시스템을 구성한다. 제안하는 해마 신경망은 호감도 조정에 의해서 입력되는 영상패턴의 특징들을 흥분학습과 억제학습을 이용하여 불필요한 특징은 억제시키고 중요한 특징은 흥분학습을 통해 장기기억 시켜서 적응성 있는 실시간 검색 시스템을 구현한다.

Tn this paper, We propose a user friendly object-based multimedia retrieval system using the HCNN(HippoCampus Neural Network. Most existing approaches to content-based retrieval rely on query by example or user based low-level features such as color, shape, texture. In this paper we perform a scene change detection and key frame extraction for the compressed video stream that is video compression standard such as MPEG. We propose a method for automatic color object extraction and ACE(Adaptive Circular filter and Edge) of content-based multimedia retrieval system. And we compose multimedia retrieval system after learned by the HCNN such extracted features. Proposed HCNN makes an adaptive real-time content-based multimedia retrieval system using excitatory teaming method that forwards important features to long-term memories and inhibitory learning method that forwards unimportant features to short-term memories controlled by impression.

키워드

참고문헌

  1. Wei Xiong and Chung-Mong Lee, 'Efficient Scene Change Detection and Camera Motion Annotation for Video Classification', Computer Vision and Image Understanding Vol.71, No. 2/2, August, pp.l66-181, 1998 https://doi.org/10.1006/cviu.1998.0711
  2. Hong Heater Yu, 'A hierarchical Multiresolution Video shot Transition Detection Scheme', Computer Vision and Image Understanding Vol.75, No. 1/2, July/August, pp. 196-213, 1999 https://doi.org/10.1006/cviu.1999.0773
  3. A. Vailaya, A. K. Jain and H. J. Zhang, 'On Image Classification: City Images vs. Landscapes', Pattern Recognition, Vol.31, pp. 1921-1936, 1998 https://doi.org/10.1016/S0031-3203(98)00079-X
  4. B. Manjunath and W. Ma, 'Texture features for browsing and retrieval of image data,' IEEE Trans. Pattern Anal. Machine Intel, Vol.18, pp. 837-842, Aug. 1996 https://doi.org/10.1109/34.531803
  5. R. Mehrotra and J. Gary, 'Similar-shape retrieval in shape data management, ' IEEE Computer, vol. 28, pp. 57-62 Sept. 1995 https://doi.org/10.1109/2.410154
  6. 후지와라 히로시 '그림으로 보는 최신 MPEG', 교보문고, 2001
  7. John S. Boreczky and Lawrence A. Rowe, 'Comparison of Video Shot Boundary Detection Techniques', Storage and Retrieval Image and Video Database Ⅳ, Proc. of IS&T/SPIE 1996 Symp. on Elec. Imaging: Science and Technology, February 1996 https://doi.org/10.1117/12.234794
  8. Arun Hampapur, Ramesh Jain and Terry Weymouth, 'Digital video Segmentation', Proc. Second Annual ACM Multimedia Conference, October, 1994 https://doi.org/10.1145/192593.192699
  9. R. C. Gonzalez, R. E.Woods, Digital image processing, Prentice-Hall, 200l
  10. John R smith and Shih-Fu Chang, 'Tools and Techniques for Color Image Retrieval', IS&T/SPIE proceedings vol. 2670, Storage & Retrieval for Image and Video Database, 1995 https://doi.org/10.1117/12.234781
  11. Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, Elements of Artificial Neural Networks, The MIT press, 1997
  12. 이케가야 유지, 이토이 시게사토, 해마, 은행나무, (2003)
  13. Dayan, P. and Abbott, L.F., Theoretical Neuroscience, MIT press, 2001
  14. Ventriglia, F. and Maio, V.D., 'Synaptic fusion pore structure and AMPA receptor activation according to Brownian simulation of glutamate diffusion, Biological Cybernetics', Vol. 88, No, 3, 2003 https://doi.org/10.1007/s00422-002-0375-5
  15. D.G. Amaral and M. P. Witter. 'The three-dimensional organization of the hippocampal formation: A review of anatomical data,' Neuroscience, vol. 31, pp.571-591, 1989 https://doi.org/10.1016/0306-4522(89)90424-7
  16. R. Miller, 'Cortico- Hippocampal interplay and the representation of contexts in the brain. Springer Verlag. 1991
  17. technique for OFDM and MC-CDMA in a multipath fading channels,' in Proc. of IEEE Conf. on Acoustics, Speech and Signal Processing, pp. 2529-2532, Munich, Germany, May 1997 https://doi.org/10.1109/ICASSP.1997.599632
  18. Serge Belongie, Chad Carson, Hayit greenspan, and Jitendra Malik, 'Color and Texture-Based Image Segmentation Using EM and Its Application to Content-based Image retrieval,' Sixth International Conference on Computer Vision, pp. 675-682, January. 1998 https://doi.org/10.1109/ICCV.1998.710790
  19. Abhijit. S. Pandy, Pattern Recognition With Neural Networks in C++, IEEE Press, 1995
  20. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R, Zabih, 'Image Indexing Using Color Correlograms,' Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997 https://doi.org/10.1109/CVPR.1997.609412
  21. Jinshan Tang and Scott Acton, 'An Image Retrieval Algorithm using Multiple Query Images,' IEEE Proc. Signal Processing and Its Applications, vol. 1, pp. 193-196, 2003 https://doi.org/10.1109/ISSPA.2003.1224673
  22. B. Ko, H. Byun, 'FRIP:a region-based image retrieval tool using automatic Image segmentation and stepwise Boolean AND matching' IEEE Trans on Multimedia, Vol.07, No.01, pp.0105-0113, 2005.02 https://doi.org/10.1109/TMM.2004.840603