References
- Patel T, Gandhi, S. A survey on context based similarity techniques for image retrieval. International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2017); 2017 Feb 21–23. p. 219–223.
- Swain MJ, Ballard DH. Color indexing. Int J Comput Vis. 1991;7(1):11–32.
- Ruikar SD, Kabade RS. Content based image retrieval by combining feature vector. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); 2016 Mar 23–25. p. 1517–1523.
- Liapis S, Tziritas, G. Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimedia. 2004;6(5):676–686.
- Lin CH, Chen RT, Chan YK. A smart content-based image retrieval system based on color and texture feature. Image Vis Comput. 2009;27(6):658–665.
- Subrahmanyam M, Jonathan QM, Wu RP, et al. Modified color motif co-occurrence matrix for image indexing and retrieval. Comput Electr Eng. 2013;39:762–774.
- Choudhary R, Raina N, Chaudhary N, et al. An integrated approach to content based image retrieval. Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference); 2014. p. 2404–2410.
- Lin CH, Chan YK, Chen KH, et al. Fast color-spatial feature based image retrieval methods. Expert Syst Appl. 2011;38(9):11412–11420.
- Raveaux R, Burie J-C, Ogier J-M. Structured representations in a content based image retrieval context. J Vis Commun Image Represent. 2013;24:1252–1268.
- Wei CH, Li Y, Chau WY, et al. Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recognit. 2009;42(3):386–394.
- Belloulata K, Belallouche L, Belalia A, et al. Region based image retrieval using shape-adaptive DCT. Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference; 2014 Jul 9–13. p. 470–474.
- Flickner M, Sawhney H, Niblack W, et al. Query by image and video content: the QBIC system. IEEE Comput. 1995;28(9):23–32.
- Deng Y, Manjunath B. NeTra-V: toward an object-based video representation. IEEE Trans Circ Syst Video Technol. 1998;8:616–627.
- Janvier B, Bruno E, Pun T, et al. Information-theoretic temporal segmentation of video and applications: multiscale keyframes selection and shot boundaries detection. Multimed Tools Appl. 2006;30(3):273–288.
- Chasanis VT, Likas AC, Galatsanos NP. Scene detection in videos using shot clustering and sequence alignment. IEEE Trans Multimedia. 2009;11(1):89–100.
- Smeaton AF, Over P, Doherty AR. Video shot boundary detection: seven years of TRECVid activity. Comput Vis Image Underst. 2010;114(4):411–418.
- Wolf W. Key frame selection by motion analysis. IEEE International Conference on Acoustics, Speech and Signal Processing; 1996 May; 2. p. 1228–1231.
- Khollam R, Pratap Singh S. A survey on content based lecture video retrieval using speech and video text information. 2015;4(1):344–346.
- Yang H, Meinel C. Content based lecture video retrieval using speech and video text information. IEEE T Learn Technol. 2014;7(2):142–154.
- Radha N. Video retrieval using speech and text in video. Inventive Computation Technologies (ICICT), International Conference; 2016 Aug 26–27.
- Wang M, Ming Y, Liu Q, et al. Image-based video retrieval using deep feature. 2017 IEEE International Conference on Smart Computing (SMARTCOMP). 2017 May 29–31. p. 1–6.
- Elmongui HG, Mokbel MF, Aref WG. Spatio-temporal histograms. Proceedings of the International Symposium on Advances in Spatial and Temporal Databases (SSTD'03), LNCS 3633. 2005. p. 19–36.
- Chen W, Zhang YJ. Parametric model for video content analysis. Pattern Recognit Lett. 2008;29(3):181–191.
- Jiang YG, Yang J, Ngo CW, et al. Representations of keypoint-based semantic concept detection: a comprehensive study. IEEE Trans Multimedia. 2010;12(1):42–53.
- Zhao WL, Ngo CW. Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection. IEEE Trans Image Process. 2009;18(2):412–423.
- Mrak M, Calic J, Kondoz A. Fast analysis of scalable video for adaptive browsing interfaces. Comput Vis Image Underst. 2009;113(3):425–434.
- Amel AM, Abdessalem BA, Abdellatif M. Video shot boundary detection using motion activity descriptor. J. Telecomm. 2010;2(1):54–59.
- Zhuang Y, Rui Y, Huang TS, et al. Adaptive key frame extraction using unsupervised clustering. IEEE International Conference on Image Processing; 1998 Oct;1. p. 866–870.
- Fayk, MB, El Nemr HA, Moussa, MM. Particle swarm optimisation based video abstraction. J Adv Res. 2010;1(2):163–167.
- Nie X, Liu J, Sun J, et al. Key-frame based robust video hashing using isometric feature mapping. J. Comput Inf Sys. 2011;7(6):2112–2119.
- Omidyeganeh M, Ghaemmaghami S, Shirmohammadi S. Video keyframe analysis using a segment-based statistical metric in a visually sensitive parametric space. IEEE Trans Image Process. 2011;20(10):2730–2737.
- de Avila SEF, Lopes, APB, da Luz A. Jr., et al. VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recognit Lett. 2011;32(1):56–68.
- Campbell M, Haubold A, Liu M, et al. IBM Research TRECVID-2007 Video Retrieval System. NIST TRECVID-2007 Workshop, Gaithersburg, Maryland, 2007 Nov.
- Natsev A, Jiang W, Merler M, et al. IBM Research TRECVID-2008 Video Retrieval System. NIST TRECVID-2008 Workshop, Gaithersburg, Maryland, 2008 Nov.
- Natsev A, Bao S, Chang J, et al. IBM Research TRECVID-2009 Video Retrieval System. NIST TRECVID-2008 Workshop, Gaithersburg, MD, 2009 Nov.
- Zhang X, Xu C, Cheng J, et al. Effective annotation and search for video blogs with integration of context and content analysis. IEEE Trans Multimedia. 2009;11(2):272–285.
- Ulges A, Schulze C, Koch M, et al. Learning automatic concept detectors from online video. Comput Vis Image Underst. 2010;114:429–438.
- Fountain SR, Tan TN. Efficient rotation invariant texture features for content-based image retrieval. Pattern Recognit. 1998;31(11):1725–1732.
- Boreczky JS, Rowe, LA. Comparison of video shot boundary detection techniques. J Electron Imaging. 1996;5(2):122–128.
- Fang H, Jiang J, Feng Y. A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit. 2006;39(11):2092–2100.
- Bose P, Laganiere R, Whitehead A. VIVA Research Lab, University of Ottawa, School of Computer Science, Carleton University, some results in video segmentation. Retrieved on 2012 Jan, from http://www.site.uottawa.ca/~laganier/videoseg/
- Whitehead A, Bose P, Laganiere R. Feature based cut detection with automatic threshold selection. International Conference on Image and Video Retrieval, CIVR 2004: Image and Video Retrieval. LNCS 3115. 2004, p. 410–418.
- Pfeiffer S, Lienhart R, Kühne G, et al. The MoCA project-movie content analysis research at the University of Mannheim. GI Jahrestagung. 1998;329–338.