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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顧孟愷 | |
| dc.contributor.author | Fu-Chun Hsu | en |
| dc.contributor.author | 許傅鈞 | zh_TW |
| dc.date.accessioned | 2021-06-13T00:08:24Z | - |
| dc.date.available | 2009-08-28 | |
| dc.date.copyright | 2007-08-28 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-27 | |
| dc.identifier.citation | [1] H.J. Zhang, Low, Smoliar. “Automatic partitioning of full-motion
video”, Multimedia Systems 1, 1993 [2] Koprinska, Carrato. “Temporal video segmentation: A survey”, Signal Processing: Image Communication,2001 [3] Faisal M.Khan et.al. “Hardware-Based Support Vector Machine Classification in Logarithmic Number Systems” Circuits and Systems, 2005, ISCAS, IEEE Symposium on [4] J. Yuan, et.al. “ A Formal Study of Shot Boundary Detection”, Circuit and Systems For Video Technology, 2007, IEEE Transaction on [5] V.Vapnik. “The National of Statistical Learning Theory”, 1st ed., New York: Springer-Verlag, 1995 50 [6] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm [7] M. Cooper and J. Foote. Scene Boundary Detection Via Video Self- Similarity Analysis. Image Processing,2001,Proc. IEEE Intl. Conf. on [8] M.Cooper, et.al. “Video Segmentation via Temporal Pattern Classification”, Transaction on Multimedia, 2007, IEEE Transaction on [9] D.Anguita, A.Boni, et.al.“A Digital Architecture for Support Vector Machines: Theory, Algorithms, and FPGA Implementation,” Neural Networks, 2003, IEEE Transaction on [10] A. Hanjalic. “Shot boundary detection: unraveled and resolved?,” Circuits Syst. Video Technol, 2002, IEEE Transaction on [11] R.Genov, et.al.“Kerneltron: Support Vector Machine in Silicon,”, Neural Networks,vol.14, no.5, pp.1426-1434, 2003, IEEE Transactions on [12] R.Herbich. Learning Kernel Classifiers:Theory and Algorithms. Cambridge: The MIT Press, 2002. [13] A. Amir, et al.,“IBM Research TRECVID-2003 Video Retrieval System” TRECVID Workshop, 2003. 51 [14] J.Yuan, et al., “A unified shot boundary detection framework based on graph partition model,” Multimedia, 2005, ACM proc. [15] Ngo, “A robust dissolve detector by support vector machine,” Multimedia, 2003, ACM proc. [16] T.Chua, et al.,“An unified framework for shot boundary detection via active learning,” Proc. ICASSP, 2003 [17] Feng, et.al., “A new general framework for shot boundary detection and key-frame extraction,” Int. Workshop Multimedia Inf. Retrieval, 2005, ACM SIGMM [18] Burges, “A tutorial on support vector machines for pattern recognition,” Data mining know. Discov.,, 1998 [19] Apostol Natsev, Milind Naphade, Jelena Tesic,“Learning the Semantics of Multimedia Queries and Concepts from a Small Number of Examples,” 2005, ACM Multimedia [20] J.S. Boreczky, L.A. Rowe, ”Comparison of video shot boundary detection techniques,” Proc of SPIE- Storage and Retrieval for Still Image and Video Databases IV, Vol. 2670, San Diego, 1996 [21] A real-time shot cut detector: Hardware implementation, L. Boussaid, et.al, “Standard & Interfaces”, 2007, ACM | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28442 | - |
| dc.description.abstract | Structural analysis of video is an essential step to automatic video content
analysis. In a general video structure, shot is the most basic unit, and it must be determined before any multimedia content-based retrieval applications. However, despite the rich research efforts in software shot detection system, there is a lack of shot detection system that is designed in hardware. In this thesis, we proposed a shot boundary detection system using hardwarebased Support-Vector-Machine(SVM). Our system can detect both cut shots and gradual shots. We optimized the feature extraction process based on global color histogram with a pipelined architecture to save memory and increase the overall speed. The class imbalance problem in shot detection is solved by using random pseudo-sampling at the SVM training stage. The digital hardware SVM is designed using a fully-parallel pipelined architecture and is highly configurable on vector dimensions. Our data wordlength only used 4 bits signed integer plus one bit decimal in fixed number, while the detection accuracy is competitive comparing with floating point software. Our SVM classifier presented here can run a speed up to 251.62MHZ on Xilinx Virtex IV XC4VSX35 FPGA, and the video processing on our shot detection system can achieve 128 fps on PC. That makes our system met the real-time constraints, and detect shots in a continous video stream such as live-TV news or sports game. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T00:08:24Z (GMT). No. of bitstreams: 1 ntu-96-R94922082-1.pdf: 805799 bytes, checksum: b3a31c34f160704a402b35328a1cd207 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Overview or shot boundary detection System . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Our Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Shot Boundary Detecton System and SVM 10 2.1 Introduction of the Shot Boundary Detection System . . . . . 11 2.1.1 Features Extraction . . . . . . . . . . . . . . . . . . . . 12 2.1.1.1 General Features ExtractionMethods . . . . 13 2.1.1.2 Temporal Kernel Correlation Filtering . . . . 15 2.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 Brief Introduction . . . . . . . . . . . . . . . . . . . . . 17 2.2.2 Shot boundary classification using SVM . . . . . . . . 19 2.2.3 Hardware Implementation of SVM . . . . . . . . . . . 20 2.2.4 Hardware Shot Detector . . . . . . . . . . . . . . . . . 21 3 System and Hardware Architecture 22 3.1 Feature Extraction in Shot Boundart Detection . . . . . . . . 23 3.1.1 Visual Representation of Video . . . . . . . . . . . . . 23 3.1.2 Continuity FeatureMeasurement . . . . . . . . . . . . 24 3.1.3 A Pipelined Temporal Kernel Correlation Architecture 25 3.2 Support Vector Machine Architecture . . . . . . . . . . . . . . 26 3.2.1 Proposed Hardware Architecture . . . . . . . . . . . . 28 3.2.1.1 Hardware design platform . . . . . . . . . . . 28 3.2.1.2 Class Imbalance in Shot Boundary Detection 29 3.2.1.3 A Fully-Parallel Pipelined SVM . . . . . . . . 30 3.2.2 Post-processing Refinement of Shot Detection System . 33 4 Experimental Result 35 4.1 EvaluationMetrics . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Training using RandomPseudo-Negative sampling . . . . . . . 36 4.3 Hardware Synthesizing Results . . . . . . . . . . . . . . . . . . 38 4.3.1 Result compares with other SVMhardwares . . . . . . 38 4.3.2 Result compares with other hardware shot detector . . 39 4.4 Wordlength Quantization . . . . . . . . . . . . . . . . . . . . . 40 4.4.1 Discussion on Effects of Quantization in Shot Detection 42 4.5 Performance of the Proposed Shot Detection System . . . . . 44 4.5.1 Realtime Shot Detection on Various Video Types . . . 45 5 Conclusion 48 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 | |
| dc.language.iso | en | |
| dc.subject | 影片分段偵測 | zh_TW |
| dc.subject | 支援向量機 | zh_TW |
| dc.subject | 硬體 | zh_TW |
| dc.subject | Support Vector Machine | en |
| dc.subject | hardware | en |
| dc.subject | shot boundary detection | en |
| dc.title | 以支援向量機硬體為基礎的影片分段界線偵測系統設計與實作 | zh_TW |
| dc.title | Design and Implementation of Hardware-SVM-based Shot Detection System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡韶逸,魏宏宇,洪士灝 | |
| dc.subject.keyword | 支援向量機,硬體,影片分段偵測, | zh_TW |
| dc.subject.keyword | Support Vector Machine,hardware,shot boundary detection, | en |
| dc.relation.page | 52 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-30 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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