중요도 기반 퍼지 원 클래스 서포트 벡터 머신을 이용한 비디오 요약 기술 = Video Summarization Using Importance-based Fuzzy One-Class Support Vector Machine

국문 초록 (Abstract)

본 논문에서는 비디오 요약을 시각적으로 특징이 있고 주관적으로 중요한 비디오 세그먼트 집합을 구하는 새로운 요약방식을 기술한다. 시각적으로 특징이 있는 데이터 포인트를 찾기 위해 novelty detection으로 잘 알려져 있는 OC-SVM(One-Class Support Vector Machine)을 사용할 수 있다. 그러나 OC-SVM의 처리과정에 비디오 세그먼트에 대한 사용자의 주관적인 중요도를 반영하기는 어렵다. OC-SVM의 처리과정에 사용자의 주관적 중요성을 반영하기 위해서, 본 논문에서는 OC-SVM의 퍼지 버전을 유도한다. IFOC-SVM(Importance-based Fuzzy One-Class Support Vector Machine)은 비디오 세그먼트의 중요도에 따라 각 데이터 포인트에 가중치를 부여하고 데이터 분포의 서포트를 측정한다. 이때, 구해진 서포트 벡터는 비디오 세그먼트의 중요도와 시각적 특징 관점에서 비디오의 내용을 축약하여 표현한다. 제안된 알고리즘의 성능을 증명하기 위하여 가상의 데이터들과 다양한 종류의 비디오들을 가지고 실험하였다. 실험 결과는 제안하는 방법의 성능이 다른 비디오요약의 성능보다 우수함을 보여주었다.

다국어 초록 (Multilingual Abstract)

In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

참고문헌 (Reference)

1 김기주, "퍼지 원 클래스 서포트 백터 머신" 한국인터넷정보학회 6 (6): 159-170, 2005

2 Yeung, M.M, "Video Visualization for Compact Presentation and Fast Browsing of Pictorial Content" 7 (7): 771-785, 1997

3 YoungSik Choi, "Video Summarization Using Fuzzy One-Class Support Vector Machine" 3043 : 49-56, 2004

4 Shingo Uchihashi, "Video Magna: Generating Semantically Meaningful Video Summaries" 383-391, 1999

5 P. Hayton, "Support vector novelty detection applied to jet engine vibration spectra" 2000

6 Asa Ben-Hur, "Support Vector Clustering" 2 : 125-137, 2001

7 J. C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines" Microsoft Research 1998

8 YoungSik Choi, "Scalable Keyframe Extraction Using One-Class Support Vector Machine. ICCS2003" 2660 : 491-499, 2003

9 Yunqiang Chen, "One-class SVM for learning in image retrieval" 34-37, 2001

10 Girgensohn, A, "Keyframe-Based User Interfaces for Digital Video" 61-67, 2001

11 Tax, D. M. J, "Image database retrieval with Support vector data description" 2000

12 Pei-Yi Hao, "Fuzzy one-class support vector machines" 159 : 2317-2336, 2008

13 J. M. Keller, "Evidence Aggregation Networks for Fuzzy Logic Inference" 3 (3): 762-769, 1992

14 B. Scholkopf, "Estimating the Support of a High-Dimensional Distribution" Microsoft Research 1999

15 Manuel Davy, "Detection of abrupt spectral changes using support vector machines: an application to audio signal segmentation" 1313-1316, 2002

16 S. W. Smoliar, "Content-based video indexing and retrieval" 62-72, 1994

17 B. Shahraray, "Automatic generation of pictorial transcripts of video program,s" 512-519, 1995

18 Nevenka Dimitrova, "Applications of Video-Content Analysis and Retrieval" 9 (9): 42-55, 2002

19 Dimitrova, N, "Applications of Video-Content Analysis and Retrieval" 9 : 42-55, 2002

20 Nello Cristianini, "An Introduction to Support Vector Machines and other kernel-based learning methos" Cambridge University Press 2000

21 Muller, K.-R, "An Introduction to Kernel-Based Learning Algorithms" 12 (12): 181-202, 2001

22 Hanjalic, A, "An Integrated Scheme for Automated Video Abstraction Based on Unsupervised Cluster-Validity Analysis" 9 (9): 1280-1289, 1999

23 Y. Zhuang, "Adaptive key frame extraction using unsupervised clustering" 866-870, 1998