Abstract
This paper proposes a novel scene analysis algorithm based on three-dimensional discrete wavelet transform (3D DWT). Based on the correlation among the adjacent frames, video frames can be considered as four categories: abrupt scene transition, motion scene, gradual scene transition and static scene, which are ranked from low to high according to the strength of the correlation. Through the investigation of the particular temporal and spatial distribution of each category, the correlation among adjacent frames could be described by the 3D DWT coefficients related statistical features, which are the energy of high-frequency coefficients difference, the sum of high-frequency coefficients magnitudes and the difference of low-frequency coefficients magnitudes. The energy of high-frequency coefficients difference is first used to detect the abrupt scene transition including cut and flashlight. Then all the three features are input to SVM for the purpose of analyzing the residual scenes and detecting the gradual scene transition, such as dissolve and fade. Experimental results show the method to be effective not only for the abrupt scene transition detection, but also for the gradual scene transition detection.
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References
Alattar AM (1993) Detecting and compressing dissolve regions in video sequences with DVI multimedia image compression algorithm. IEEE Int Symp Circuits Syst (ISCAS) 1:13–16
Antonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using the wavelet transform. IEEE Trans Image Process 1(2):205–220
Arman F, Hsu A, Chiu MY (1993) Feature management for large video databases. Proceeding Storage and Retrieval for Image and Video Databases I, SPIE 1908:2–12
Babu RV, Ramakrishnan KR (2002) Compressed domain motion segmentation for video object extraction. Proceeding IEEE Int Conf Acoust Speech Signal Process 4:3788–3791
Bouthemy P, Gelgon M, Ganansia F (1999) A unified approach to shot change detection and camera motion characterization. IEEE Trans Circuits Syst Video Technol 9(7):1030–1044
Connor NO, Sav S, Adamek T, Mezaris V, Kompatsiaris I, Lui TY, Izquierdo E, Bennstrom CF, Casas JR (2003) Region and object segmentation algorithms in the qimera segmentation platform. Proceeding 3rd International workshop Content-Based Multimedia Indexing (CBMI03), pp 381–388
Coudert F, Benois-Pineau J, Barba D (1998) Dominant motion estimation and video partitioning with a 1D signal approach. Proceeding SPIE Conference on Multimedia storage and Archiving systems III 3527:283–294
Fernando WAC, Canagarajah CN (1999) Automatic detection of fade-in and fade-out in video sequences. IEEE International Symposium on Circuit and System, pp 255–258
Fernando WAC, Canagrajah CN, Bull DR (1999) Fade and dissolve detection in uncompressed and compressed video sequence. Proc - Int Conf Image Proc 3:299–303
Fernando WAC, Canagararajah CN, Bull DR (2000) Fade-in and fade-out detection in video sequences using histograms. Proc IEEE Int Symp Circ Syst 4:709–712
Gargi U, Kasturi R, Strayer SH (2000) Performance characterization of video-shot-change detection methods. IEEE Trans Circuits Syst Video Technol 10(2):1–13
Guimarães SJF, Couprie M, de Araújo A, Leite NJ (2003) Video segmentation based on 2D image analysis. Pattern Recogn Lett 24(7):947–957
Hampapur A, Jain R, Weymouth T (1995) Production model based digital video segmentation. Multimedia Tools and Application 1(1):9–46
Heng WJ, Ngan KN (1999) Integrated shot boundary detection using object-based techniques. Proc - Int Conf Image Proc 3:289–293
Heng WJ, Ngan KN (2003) High accuracy flashlight scene determination for shot boundary detection. Signal Process Image Commun 18(3):203–219
Hsu CW, Chang CC, Lin CJ (2004) A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
ISO/IEC, ISO/IEC 15444-1, Information technology-JPEG 2000 image coding system-Part 1: Core coding system. http://www.jpeg.org, 2003
Jamrozik ML, Hayes MH (2002) A compressed domain video object segmentation system. Proc IEEE Int Conf Image Process 1:113–116
Joyce RA, Liu B (2006) Temporal segmentation of video using frame and histogram space. IEEE Trans Multimedia 8:130–140
Klock H, Polzer A, Buhmann JM (1997) Region-Based Motion Compensated 3D-Wavelet Transform Coding of Video. Proc - Int Conf Image Proc 2:776
Krämer P, Benois-Pineau J, Domenger JP (2006) Scene Similarity Measure for Video Content Segmentation in the Framework of Rough Indexing Paradigm. Int J Intell Syst 21(7):765–783
Lam CF, Lee MC (1998) Video segmentation using color difference histogram. Lecture Notes in Computer Science 1464. Springer-Verlag, New York, pp 159–174
Li Z, Liu G (2008) A novel scene change detection based on 3D wavelet transform. IEEE International Conference on Image Processing, pp 1536–1539
Li Y, Gao X, Ji H (2003) A 3D wavelet based spatialtemporal approach for video watermarking, Proceeding of 5th International Conference on Computational Intelligence and Multimedia Applications, pp 260–265
Lienhart R (2001) Reliable transition detection in videos: a survey and practitioner’s guide. Int J Image Graph 1(3):469–486
Luo L, Wu F, Li S, Xiong Z, Zhuang Z (2004) Advanced motion threading for 3D wavelet video coding. Signal Process Image Commun 19(7):60l–6l6
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet transform. IEEE Trans Pattern Anal Mach Intell 11(7):674–693
Meng J, Juan Y, Chang SF (1995) Scene change detection in a MPEG-compressed video sequence. Proceeding SPIE Digital Video Compression: Algorithms and Technologies 2419:14–25
Mezaris V, Kompatsiaris I, Boulgouris NV, Strintzis MG (2004) Real time compressed domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Trans Circuits Syst Video Technol 14(5):606–612
Nakajima Y (1994) A video browsing using fast scene cut detection for an efficient networked video database access. IEICE Trans Inf Syst E77-D(12):1355–1364
Park JH, Park SY, Kang SJ, Cho WH (2003) Content-based scene change detection of video sequence using hierarchical hidden Markov model. Lect Notes Comput Sci 2843:426–433
Pei SC, Chou YZ (1999) Efficient MPEG compressed video analysis using macroblock type information. IEEE Trans Multimedia 1(4):321–333
Pei SC, Chou YZ (2002) Effective wipe detection in MPEG compressed video using macro block type information. IEEE Trans Multimedia 4(3):309–319
Primaux L, Benois-Pineau J, Krämer P, Domenger JP (2004) Shot boundary detection in the framework of rough indexing paradigm. In TREC Video Retrieval Evaluation Online Proceedings, TRECVID’04
Qian X, Liu G, Su R (2006) Effective Fades and Flashlight Detection Based on Accumulating Histogram Difference. IEEE Trans Circuits Syst Video Technol 16(10):1245–1258
Shen K, Delp EJ (1995) A fast algorithm for video parsing using MPEG compressed sequences. IEEE International Conference on Image Processing, pp 252–255
Sifakis E, Tziritas G (2001) Moving object localization using a multilabel fast marching algorithm. Signal Process Image Commun 16:963–976
Truong BT, Venkatesh S (2001) Determining dramatic intensification via flashing lights in movies. Proceeding IEEE International Conference Multimedia Expo, pp 60–63
Wang R, Zhang HJ, Zhang YQ (2000) A confidence measure based moving object extraction system built for compressed domain. Proc IEEE Int Symp Circ Syst 5:21–24
Wang J, Xu Y, Yu S, Zhou Y (2005) Flashlight scene detection for MPEG videos. IEEE 7th workshop on Multimedia Signal Processing, pp 1–4
Yeo B, Liu B (1995) Rapid scene analysis on compressed video. IEEE Trans Circuits Syst Video Technol 5(6):533–544
Yi X, Ling N (2005) Fast pixel-based video scene change detection. Proceeding IEEE International Symposium on Circuits and Systems (ISCAS 2005), pp 3443–3446
Yuan J, Wang H, Xiao L, Zheng W, Li J, Lin F, Zhang B (2007) A Formal Study of Shot Boundary Detection. IEEE Trans Circuits Syst Video Technol 17(2):168–186
Zabih R, Miller J, Mai K (1995) A feature-based algorithm for detecting and classifying scene breaks. Proceeding ACM Multimedia, pp 189–200
Zabih R, Miller J, Mai K (1999) A feature-based algorithm for detecting and classification production effects. Multimedia Syst 7:119–128
Zhang H, Kankanhalli A, Smoliar S (1993) Automatic partitioning of full-motion video. ACM/Springer Multimedia Systems, pp 10–28
Zhang D, Qi W, Zhang HJ (2001) A new shot boundary detection algorithm. Proceeding Second IEEE Pac Rim Conf Multimed 2195:63–70
Acknowledgements
This work is supported in part by the National 973 Project (No.2007CB311002), National 863 Project (No.2009AA01Z409), and National Natural Science Foundation of China Project (NSFC, No.60903121).
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Li, Z., Liu, G. Video scene analysis in 3D wavelet transform domain. Multimed Tools Appl 56, 419–437 (2012). https://doi.org/10.1007/s11042-010-0594-z
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DOI: https://doi.org/10.1007/s11042-010-0594-z