Advance on large scale near-duplicate video retrieval

doi:10.1007/s11704-019-8229-7

Front. Comput. Sci.

2020, Vol. 14

Issue (5) : 145702 https://doi.org/10.1007/s11704-019-8229-7

REVIEW ARTICLE

Advance on large scale near-duplicate video retrieval

Ling SHEN¹, Richang HONG¹(

), Yanbin HAO^1,²

¹. 1School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
². 2Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China

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Abstract

Emerging Internet services and applications attract increasing users to involve in diverse video-related activities, such as video searching, video downloading, video sharing and so on. As normal operations, they lead to an explosive growth of online video volume, and inevitably give rise to the massive near-duplicate contents. Near-duplicate video retrieval (NDVR) has always been a hot topic. The primary purpose of this paper is to present a comprehensive survey and an updated reviewof the advance on large-scaleNDVR to supply guidance for researchers. Specifically, we summarize and compare the definitions of near-duplicate videos (NDVs) in the literature, analyze the relationship between NDVR and its related research topics theoretically, describe its generic framework in detail, investigate the existing state-of-the-art NDVR systems. Finally, we present the development trends and research directions of this topic.

Keywords near-duplicate videos video retrieval featurerepresentation video signature indexing similarity measurement

Corresponding Author(s): Richang HONG

Just Accepted Date: 23 August 2019 Issue Date: 10 March 2020

Cite this article:

Ling SHEN,Richang HONG,Yanbin HAO. Advance on large scale near-duplicate video retrieval[J]. Front. Comput. Sci., 2020, 14(5): 145702.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-8229-7
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I5/145702

1	N Khan, I Yaqoob, I A T Hashem, Z Inayat, W K M Ali, M Alam, M Shiraz, A Gani. Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014, 2014: 712826 https://doi.org/10.1155/2014/712826
2	X Wu, A G Hauptmann, C W Ngo. Practical elimination of nearduplicates from web video search. In: Proceedings of the 15th ACM International Conference on Multimedia. 2007, 218–227 https://doi.org/10.1145/1291233.1291280
3	J Davidson, B Liebald, J Liu, P Nandy, T V Vleet. The youtube video recommendation system. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 293–296 https://doi.org/10.1145/1864708.1864770
4	B Yang, T Mei, X S Hua, L Yang, S Q Yang, M J Li. Online video recommendation based on multimodal fusion and relevance feedback. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval. 2007, 73–80 https://doi.org/10.1145/1282280.1282290
5	E Koch, J Rindfre, J Zhao. Copyright protection for multimedia data. In: Proceedings of the International Conference on Digital Media and Electronic Publishing. 1994
6	X Zhou, L Chen . Monitoring near duplicates over video streams. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 521–530 https://doi.org/10.1145/1873951.1874020
7	J J Tamilselvi, C B Gifta. Handling duplicate data in data warehouse for data mining. International Journal of Computer Applications, 2011, 15(4): 7–15 https://doi.org/10.5120/1939-2590
8	M S Chen, J Han, P S Yu. Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 2002, 8(6): 866–883 https://doi.org/10.1109/69.553155
9	X Wu, I Ide, S Satoh. News topic tracking and re-ranking with query expansion based on near-duplicate detection. In: Proceedings of Pacific-Rim Conference on Multimedia. 2009, 755–766 https://doi.org/10.1007/978-3-642-10467-1_66
10	H T Shen, X Zhou, Z Huang, J Shao, X Zhou. UQLIPS: a realtime near-duplicate video clip detection system. In: Proceedings of the 33rd International Conference on Very Large Data Bases. 2007, 1374–1377
11	J Liu, Z Huang, H Cai, H T Shen, C W Ngo, g W Wan. Near-duplicate video retrieval: current research and future trends. ACM Computing Surveys, 2013, 45(4): 44 https://doi.org/10.1145/2501654.2501658
12	M Cherubini, R D Oliveira, N Oliver. Understanding near-duplicate videos: a user-centric approach. In: Proceedings of the 17th ACM International Conference on Multimedia. 2009, 35–44 https://doi.org/10.1145/1631272.1631280
13	C L Chou, H T Chen, S Y Lee. Pattern-based near-duplicate video retrieval and localization on web-scale videos. IEEE Transactions on Multimedia, 2015, 17(3): 382–395 https://doi.org/10.1109/TMM.2015.2391674
14	J R Zhang, J Y Ren, F Chang, T L Wood, J R Kender. Fast nearduplicate video retrieval via motion time series matching. In: Proceedings of the IEEE International Conference on Multimedia and Expo. 2012, 842–847 https://doi.org/10.1109/ICME.2012.111
15	A Basharat, Y Zhai, M Shah. Content based video matching using spatiotemporal volumes. Computer Vision and Image Understanding, 2008, 110(3): 360–377 https://doi.org/10.1016/j.cviu.2007.09.016
16	A W M Smeulders, M Worring, S Santini, A Gupta, R Jain. Contentbased image retrieval at the end of the early. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1349–1380 https://doi.org/10.1109/34.895972
17	Y Yan, B C Ooi, A Zhou. Continuous content-based copy detection over streaming videos. In: Proceedings of the 24th IEEE International Conference on Data Engineering. 2008, 853–862 https://doi.org/10.1109/ICDE.2008.4497494
18	L Mou, T Huang, Y Tian, M Jiang, W Gao. Content-based copy detection through multimodal feature representation and temporal pyramid matching. ACM Transactions on Multimedia Computing Communications and Applications, 2013, 10(1): 1–20 https://doi.org/10.1145/2542205.2542208
19	R Hong, Y Yang, M Wang, X S Hua. Learning visual semantic relationships for efficient visual retrieval. IEEE Transactions on Big Data, 2017, 1(4): 152–161 https://doi.org/10.1109/TBDATA.2016.2515640
20	M S G Saravanan, M T Sivaprakasam, D Somasundaram. A review on content based video retrieval, classification and summarization. Asian Journal of Applied Science and Technology, 2017, 1(9): 40–45
21	Q Xie, Z Huang, H T Shen, X Zhou, C Pang. Efficient and continuous near-duplicate video detection. In: Proceedings of the 12th International Asia-Pacific Web Conference. 2010, 260–266 https://doi.org/10.1109/APWeb.2010.72
22	X Nie, Y Chai, J Liu, J Sun, Y Yin. Spherical torus-based video hashing for near-duplicate video detection. Science China Information Sciences, 2016, 59(5): 059101 https://doi.org/10.1007/s11432-016-5528-6
23	H B da Silva, Z K do Patrocínio, G Gravier, L Amsaleg, A D A Araújo, S J F Guimaraes. Near-duplicate video detection based on an approximate similarity self-join strategy. In: Proceedings of the 14th International Workshop on Content-Based Multimedia Indexing. 2016, 1–6 https://doi.org/10.1109/CBMI.2016.7500278
24	S Lameri, L Bondi, P Bestagini, S Tubaro. Near-duplicate video detection exploiting noise residual traces. In: Proceedings of the IEEE International Conference on Image Processing. 2017, 1497–1501 https://doi.org/10.1109/ICIP.2017.8296531
25	K Washino, B H Schwab. Video monitoring and conferencing system. U.S. Patent No. 5,625,410. 1997-4-29
26	J Jiang, Y Tong, H Lu, B Cui, K Lei, L Yu. GVoS: a general system for near-duplicate video-related applications on storm. ACM Transactions on Information Systems, 2017, 36(1): 3 https://doi.org/10.1145/3041657
27	Z Huang, L Wang, H T Shen, J Shao, X Zhou. Online near-duplicate video clip detection and retrieval: an accurate and fast system. In: Proceedings of the 25th IEEE International Conference on Data Engineering. 2009, 1511–1514 https://doi.org/10.1109/ICDE.2009.17
28	W Kraaij, G Awad. TRECVID 2011 content-based copy detection: task overview. Online Proceedings of TRECVid, 2011
29	G Awad, J Fiscus, W Kraaij. TRECVID 2011–an overview of the goals, tasks, data, evaluation mechanisms, and metrics. National Institute of Standards and Technology, 2014, 1–58 https://doi.org/10.1145/2629531
30	A F Smeaton, P Over, W Kraaij. Evaluation campaigns and TRECVid. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval. 2006, 321–330 https://doi.org/10.1145/1178677.1178722
31	J Law-To, L Chen, A Joly, I Laptev, O Buisson, V Gouet-Brunet, N Boujemaa, F Stentiford. Video copy detection: a comparative study. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval. 2007, 371–378 https://doi.org/10.1145/1282280.1282336
32	A Hampapur, R M Bolle. Comparison of sequence matching techniques for video copy detection. In: Proceedings of SPIE Storage and Retrieval for Media Databases. 2002, 194–202
33	J Zobel, T C Hoad. Detection of video sequences using compact signatures. ACM Transactions on Information Systems, 2006, 24(1): 1–50 https://doi.org/10.1145/1125857.1125858
34	A Joly, O Buisson , C Frelicot. Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Transactions on Multimedia, 2007, 9(2): 293–306 https://doi.org/10.1109/TMM.2006.886278
35	M C Yeh, K T Cheng. Video copy detection by fast sequence matching. In: Proceedings of the ACM International Conference on Image and Video Retrieval. 2009, 45 https://doi.org/10.1145/1646396.1646449
36	W Kraaij, G Awad, P Over. TRECVID-2008 content-based copy detection task overview (slides). National Institute of Standards and Technology, 2008
37	P Aigrain, H Zhang, D Petkovic. Content-based representation and retrieval of visual media: a state-of-the-art review. Multimedia Tools and Applications, 1996, 3(3): 179–202 https://doi.org/10.1007/BF00393937
38	W Hu, N Xie, L Li, S Maybank. A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems Man and Cybernetics, Part C, 2011, 41(6): 797–819 https://doi.org/10.1109/TSMCC.2011.2109710
39	R Hong, J Tang, H K Tan, C W Ngo, S Yan, T S Chua. Beyond search: event-driven summarization for web videos. ACM Transactions on Multimedia Computing Communications and Applications, 2011, 7(4): 35 https://doi.org/10.1145/2043612.2043613
40	T S Chua, R Hong, G Li, J Tang. From text question-answering to multimedia QA on web-scale media resources. In: Proceedings of the 1st ACM Workshop on Large-Scale Multimedia Retrieval and Mining. 2009, 51–58 https://doi.org/10.1145/1631058.1631069
41	W L Zhao, C W Ngo, H K Tan, X Wu. Near-duplicate keyframe identification with interest point matching and pattern learning. IEEE Transactions on Multimedia, 2007, 9(5): 1037–1048 https://doi.org/10.1109/TMM.2007.898928
42	X Wu, W L Zhao, C W Ngo. Near-duplicate keyframe retrieval with visual keywords and semantic context. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval. 2007, 162–169 https://doi.org/10.1145/1282280.1282309
43	P Geetha, V Narayanan. A survey of content-based video retrieval. Journal of Computer Science, 2008, 4(6): 734 https://doi.org/10.3844/jcssp.2008.474.486
44	X Wu, W L Zhao, C W Ngo. Efficient near-duplicate keyframe retrieval with visual language models. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2007, 500–503 https://doi.org/10.1109/ICME.2007.4284696
45	C Yeo, Y W Zhu, Q Sun, S F Chang. A framework for sub-window shot detection. In: Proceedings of the 11th International Multimedia Modelling Conference. 2005, 84–91
46	S Satoh, M Takimoto, J Adachi. Scene duplicate detection from videos based on trajectories of feature points. In: Proceedings of the International Workshop on Multimedia Information Retrieval. 2007, 237–244 https://doi.org/10.1145/1290082.1290115
47	R Hong, M Wang, M Xu, S Yan, T S Chua. Dynamic captioning: video accessibility enhancement for hearing impairment. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 421–430 https://doi.org/10.1145/1873951.1874013
48	M Wang, R Hong, X T Yuan, S Yan, T S Chua. Movie2Comics: towards a lively video content presentation. IEEE Transactions on Multimedia, 2012, 14(3): 858–870 https://doi.org/10.1109/TMM.2012.2187181
49	S T Birchfield, S Rangarajan. Spatiograms versus histograms for region-based tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 1158–1163
50	J Li, W Wu, T Wang, Y Zhang. One step beyond histograms: image representation using Markov stationary features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1–8
51	L Shang, K P Chan, X S Hua. Real-time large scale near-duplicate web video retrieval. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 531–540 https://doi.org/10.1145/1873951.1874021
52	J Song, Y Yang, Z Huang, H T Shen, J Luo. Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Transactions on Multimedia, 2013, 15(8): 1997–2008 https://doi.org/10.1109/TMM.2013.2271746
53	M J Swain, D H Ballard. Color indexing. International Journal of Computer Vision, 1991, 7(1): 11–32 https://doi.org/10.1007/BF00130487
54	D N Bhat, S K Nayar. Ordinal measures for image correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(4): 415–423 https://doi.org/10.1109/34.677275
55	W Dong, Z Wang, M Charikar, K Li. Efficiently matching sets of features with random histograms. In: Proceedings of the 16th ACM International Conference on Multimedia. 2008, 179–188 https://doi.org/10.1145/1459359.1459384
56	Y Ke, R Sukthankar. PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 506–513
57	Y Ke, R Sukthankar, L Huston. Efficient near-duplicate detection and sub-image retrieval. In: Proceedings of ACM International Conference on Multimedia. 2004 https://doi.org/10.1145/1027527.1027729
58	D G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110 https://doi.org/10.1023/B:VISI.0000029664.99615.94
59	D G Lowe. Object recognition from local scale-invariant features. In: Proceedings of IEEE International Conference on Computer Vision. 1999, 1150–1157 https://doi.org/10.1109/ICCV.1999.790410
60	H Bay, T Tuytelaars, L Van Gool. SURF: speeded up robust features. In: Proceedings of European Conference on Computer Vision. 2006, 404–417 https://doi.org/10.1007/11744023_32
61	G Yang, N Chen, Q Jiang. A robust hashing algorithm based on SURF for video copy detection. Computers and Security, 2012, 31(1): 33–39 https://doi.org/10.1016/j.cose.2011.11.004
62	Y Hao, T Mu, R Hong, M Wang, N An, J Y Goulermas. Stochastic multiview hashing for large-scale near-duplicate video retrieval. IEEE Transactions on Multimedia, 2017, 19(1): 1–14 https://doi.org/10.1109/TMM.2016.2610324
63	G Zhao, M Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 915–928 https://doi.org/10.1109/TPAMI.2007.1110
64	Y, Hao T Mu, J Y Goulermas, J Jiang, R Hong, M Wang. Unsupervised t-distributed video hashing and its deep hashing extension. IEEE Transactions on Image Processing, 2017, 26(11): 5531–5544 https://doi.org/10.1109/TIP.2017.2737329
65	O Chum, J Philbin , A Zisserman. Near duplicate image detection: min-hash and TF-IDF weighting. In: Proceedings of the British Machine Vision Conference. 2008, 812–815 https://doi.org/10.5244/C.22.50
66	W Jing, X Nie, C Cui, X Xi, G Yang, Y Yin. Global-view hashing: harnessing global relations in near-duplicate video retrieval. World Wide Web, 2019, 22(2): 771–789 https://doi.org/10.1007/s11280-018-0536-7
67	X Nie, X Li, J Sun, Y Yin. UFvH: unified feature video hashing for near-duplicate video retrieval. In: Proceedings of the Workshop on Visual Analysis in Smart and Connected Communities. 2017, 17–24 https://doi.org/10.1145/3132734.3132738
68	J Law-To, O Buisson, V Gouet-Brunet , N Boujemaa. Robust voting algorithm based on labels of behavior for video copy detection. In: Proceedings of the 14th ACM International Conference on Multimedia. 2006, 835–844 https://doi.org/10.1145/1180639.1180826
69	J R Zhang, J Y Ren, F Chang, T L Wood, J R Kender. Fast nearduplicate video retrieval via motion time series matching. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2012, 842–847 https://doi.org/10.1109/ICME.2012.111
70	C L Chou, H T Chen, Y C Chen, C P Ho, S Y Lee. Near-duplicate video retrieval and localization using pattern set based dynamic programming. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2013, 1–6
71	X S Hua, X Chen, H J Zhang. Robust video signature based on ordinal measure. In: Proceedings of International Conference on Image Processing. 2004, 685–688
72	A Krizhevsky, I Sutskever, G E Hinton. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 15th International Conference on Neural Information Processing Systems. 2012, 1097–1105
73	A S Razavian, J Sullivan, A Maki, S Carlsson. A baseline for visual instance retrieval with deep convolutional networks. In: Proceedings of International Conference on Learning Representations. 2015
74	A S Razavian, H Azizpour, J Sullivan, S Carlsson. CNN features offthe- shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014, 806–813 https://doi.org/10.1109/CVPRW.2014.131
75	Z Xu, Y Yang, A G Hauptmann. A discriminative CNN video representation for event detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1798–1807 https://doi.org/10.1109/CVPR.2015.7298789
76	G Kordopatis-Zilos, S Papadopoulos, I Patras, Y Kompatsiaris. Nearduplicate video retrieval by aggregating intermediate CNN layers. In: Proceedings of International Conference on Multimedia Modeling. 2017, 251–263 https://doi.org/10.1007/978-3-319-51811-4_21
77	D Tran, L Bourdev, R Fergus, L Torresani, M Paluri. Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 4489–4497 https://doi.org/10.1109/ICCV.2015.510
78	I Sutskever, O Vinyals, Q V Le. Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 3104–3112
79	H Zhang, M Wang, R Hong, T S Chua. Play and rewind: optimizing binary representations of videos by self-supervised temporal hashing. In: Proceedings of the 2016 ACM Multimedia Conference. 2016, 781–790 https://doi.org/10.1145/2964284.2964308
80	S Hochreiter, J Schmidhuber. Long short-term memory. Neural Computation, 1997, 9(8), 1735–1780 https://doi.org/10.1162/neco.1997.9.8.1735
81	K Cho, B Van Merriénboer, C Gulcehre, D Bahdanau, F Bougares, H Schwenk, Y Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014, 1724–1734 https://doi.org/10.3115/v1/D14-1179
82	J Song, Y Yang, Z Huang, H T Shen, R Hong. Multiple feature hashing for real-time large scale near-duplicate video retrieval. In: Proceedings of the 19th ACM International Conference on Multimedia. 2011, 423–432 https://doi.org/10.1145/2072298.2072354
83	W L Zhao, S Tan, C W Ngo. Large-scale near-duplicate web video search: challenge and opportunity. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2009, 1624–1627 https://doi.org/10.1109/ICME.2009.5202830
84	Y G Jiang, C W Ngo. Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval. Computer Vision and Image Understanding, 2009, 113(3): 405–414 https://doi.org/10.1016/j.cviu.2008.10.002
85	L Liu, W Lai, X S Hua, S Q Yang. Video histogram: a novel video signature for efficient web video duplicate detection. In: Proceedings of International Conference on Multimedia Modeling. 2007, 94–103 https://doi.org/10.1007/978-3-540-69429-8_10
86	Z Huang, H T Shen, J Shao, X Zhou. Bounded coordinate system indexing for real-time video clip search. ACM Transactions on Information Systems, 2009, 27(3): 17 https://doi.org/10.1145/1508850.1508855
87	H T Shen, B C Ooi, X Zhou. Towards effective indexing for very large video sequence database. In: Proceedings of the 2005 ACMSIGMOD International Conference on Management of Data. 2005, 730–741 https://doi.org/10.1145/1066157.1066240
88	G Kordopatis-Zilos, S Papadopoulos, I Patras, Y Kompatsiaris. Nearduplicate video retrieval with deep metric learning. In: Proceedings of the IEEE International Conference on Computer Vision. 2017, 347–356 https://doi.org/10.1109/ICCVW.2017.49
89	C Böhm, S Berchtold, D A Keim. Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Computing Surveys, 2001, 33(3): 322–373 https://doi.org/10.1145/502807.502809
90	C G M Snoek, M Worring. Multimodal video indexing: a review of the state-of-the-art. Multimedia Tools and Applications, 2005, 25(1): 5–35 https://doi.org/10.1023/B:MTAP.0000046380.27575.a5
91	S Boughorbel, J P Tarel, N Boujemaa. Generalized histogram intersection kernel for image recognition. In: Proceedings of IEEE International Conference on Image Processing. 2005, 3: III–161 https://doi.org/10.1109/ICIP.2005.1530353
92	J Wu, J M Rehg. Beyond the Euclidean distance: creating effective visual codebooks using the histogram intersection kernel. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 630–637
93	H V Jagadish, B C Ooi, K L Tan, C Yu, R Zhang. iDistance: an adaptive B+-tree based indexing method for nearest neighbor search. ACM Transactions on Database Systems, 2005, 30(2): 364–397 https://doi.org/10.1145/1071610.1071612
94	R Bayer, E Mccreight. Organization and Maintenance of Large Ordered Indexes. Software Pioneers, Springer, Berlin, Heidelberg, 2002, 245–262 https://doi.org/10.1007/978-3-642-59412-0_15
95	C Bohm, M Gruber, P Kunath, A Pryakhin, M Schubert. Prover: probabilistic video retrieval using the gauss-tree. In: Proceedings of the 23rd IEEE International Conference on Data Engineering. 2007, 1521–1522 https://doi.org/10.1109/ICDE.2007.369063
96	M Chen, S Mao, Y Liu. Big data: a survey. Mobile Networks and Applications, 2014, 19(2): 171–209 https://doi.org/10.1007/s11036-013-0489-0
97	J Wang, T Zhang, J Song, N Sebe, H T Shen. A survey on learning to hash. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 769–790 https://doi.org/10.1109/TPAMI.2017.2699960
98	J Wang, H T Shen, J Song, J Song, J Ji . Hashing for similarity search: a survey. 2014, arXiv preprint arXiv:1408.2927
99	X Zhou, L Chen, X Zhou. Structure tensor series-based large scale near-duplicate video retrieval. IEEE Transactions on Multimedia, 2012, 14(4): 1220–1233 https://doi.org/10.1109/TMM.2012.2194481
100	Y Wang, M Belkhatir, B Tahayna. Near-duplicate video retrieval based on clustering by multiple sequence alignment. In: Proceedings of the 20th ACM International Conference on Multimedia. 2012, 941–944 https://doi.org/10.1145/2393347.2396352
101	H K Tan, C W Ngo, T S Chua. Efficient mining of multiple partial near-duplicate alignments by temporal network. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(11): 1486–1498 https://doi.org/10.1109/TCSVT.2010.2077531
102	C W Ngo, W L Zhao, Y G Jiang. Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation. In: Proceedings of the 14th ACM International Conference on Multimedia. 2006, 845–854 https://doi.org/10.1145/1180639.1180827
103	M Donoser, H Bischof. Diffusion processes for retrieval revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013, 1320–1327 https://doi.org/10.1109/CVPR.2013.174
104	S Bai, X Bai, Q Tian, L J Latecki. Regularized diffusion process on bidirectional context for object retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(5): 1213–1226 https://doi.org/10.1109/TPAMI.2018.2828815
105	T Mei, Y Rui, S Li, Q Tian. Multimedia search reranking: a literature survey. ACM Computing Surveys, 2014, 46(3): 38 https://doi.org/10.1145/2536798
106	S Bai, X Bai. Sparse contextual activation for efficient visual reranking. IEEE Transactions on Image Processing, 2016, 25(3): 1056–1069 https://doi.org/10.1109/TIP.2016.2514498
107	P Over, G Awad, M Michel, J Fiscus, W Kraaij, A F Smeaton. TRECVID 2009- goals, tasks, data, evaluation mechanisms and metrics. TRECVID 2009 papers, 2010, 1–42
108	J Law-To, A Joly, N Boujemaa. Muscle-VCD-2007: a live benchmark for video copy detection. Google Scholar, 2007
109	J Ren, F Chang, T Wood, J R Zhang. Efficient video copy detection via aligning video signature time series. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval. 2012, 14 https://doi.org/10.1145/2324796.2324814
110	A Karpenko, P Aarabi. Tiny videos: a large data set for nonparametric video retrieval and frame classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3): 618 https://doi.org/10.1109/TPAMI.2010.118
111	H K Tan, X Wu, C W Ngo, W L Zhao. Accelerating near-duplicate video matching by combining visual similarity and alignment distortion. In: Proceedings of the 16th ACM International Conference on Multimedia. 2008, 861–864 https://doi.org/10.1145/1459359.1459506
112	X Wu, C W Ngo, A G Hauptmann, H K Tan. Real-rime near-duplicate elimination for web video search with content and context. IEEE Transactions on Multimedia, 2009, 11(2): 196–207 https://doi.org/10.1109/TMM.2008.2009673
113	J Venna, J Peltonen, K Nybo, H Aidos, S Kaski. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research, 2010, 11(1): 451–490
114	G E Hinton, S T Roweis. Stochastic neighbor embedding. In: Proceedings of the 15th International Conference on Neural Information Processing Systems. 2003, 857–864
115	L V D Maaten, G Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(Nov): 2579–2605
116	C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, V Vanhoucke, A Rabinovich. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1–9 https://doi.org/10.1109/CVPR.2015.7298594
117	S R Ali, J Sullivan, A Maki, S Carlsson. A baseline for visual instance retrieval with deep convolutional networks. In: Proceedings of International Conference on Learning Representations. 2015
118	L Zheng, Y Zhao, S Wang, J Wang, Q Tian. Good practice in CNN feature transfer. 2016, arXiv preprint arXiv:1604.00133
119	Y Peng, J Qi, Y Yuan. CM-GANs: cross-modal generative adversarial networks for common representation learning. ACM Transactions on Multimedia Computing, Communications, and Applications, 2019, 15(1): 22 https://doi.org/10.1145/3284750
120	J Zhang, Y Peng, M Yuan. SCH-GAN: semi-supervised cross-modal hashing by generative adversarial network. IEEE Transactions on Cybernetics, 2018

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