Rokia Missaoui
ABSTRACT
This paper is dedicated to revisiting image and video mining techniques from the viewpoint of image modeling approaches, which constitute the theoretical basis for these techniques. The most important areas belonging to image or video mining are: image knowledge extraction, content-based image retrieval, video retrieval, video sequence analysis, change detection, model learning, as well as object recognition. Traditionally, these areas have been developed independently, and hence have not benefited from some common sense approaches which provide potentially optimal and time-efficient solutions. Two different types of input data for knowledge extraction from an image collection or video sequences are considered: original image or symbolic (model) description of the image. Several basic models are described briefly and compared with each other in order to find effective solutions for the image and video mining problems. They include feature-based models and object-related structural models for the representation of spatial and temporal entities (objects, scenes or events).
- Al-Khatib, W., Day, Y. F., Ghafoor, A., and Berra, P. B. "Semantic modeling and knowledge representation in multimedia databases", IEEE Trans. Knowledge and Data Engineering, Vol. 11, No. 1, pp. 64--80, 1999. Google Scholar
Digital Library
- Alon, J., Sclaroff, S., Kollios, G. and Pavlovic, V. "Discovering Clusters in Motion Time-Series Data," Proc. IEEE Computer Vision and Pattern Recognition Conf., 2003. Google ScholarDigital Library
- Belongie, S., Carson, C., Greenspan, H. and Malik, J. "Color and texture-based image segmentation using EM and its application to context-based image retrieval", Proc. Int. Conf. on Computer Vision, pp. 675--682, 1998. Google ScholarDigital Library
- Berretti, S., Del Bimbo, A. and Vicario, E. "Efficient matching and indexing of graph models in content-based retrieval", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, No. 10, pp. 1069--1104, 1998. Google ScholarDigital Library
- Bhaskaran V. and Konstantinides, K. Image and Video Compression Standards: Algorithms and Architectures, Kluwer Academic, 1995. Google ScholarDigital Library
- Christmas, W. J., Kittler, J. and Petrou, M. "Structural matching in computer vision using probabilistic relaxation", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, pp. 749--764, 1995. Google ScholarDigital Library
- Cootes, T. F., Edwards, G. J. and Taylor, C. J. "Active appearance models", IEEE Transactions on Pattern Recognition and Machine Intelligence Vol. 23, No. 6, pp. 681--685, 2001. Google ScholarDigital Library
- Cross G. R. and Jain, A. K. "Markov random field texture models", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 5, No. 1, pp. 25--39, 1983.Google ScholarDigital Library
- Del Bimbo, A. and Pala, P. "Visual image retrieval by elastic matching of user sketches", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 2, pp. 121--132, 1997. Google ScholarDigital Library
- Djeraba, Ch. "Association and content-based retrieval", IEEE Trans. Knowledge and Data Engineering, Vol. 15, No. 1, pp. 118--135, 2003. Google ScholarDigital Library
- Dvir, G., Greenspan, H. and Rubner, Y. "Context-based image modelling", Proc. Int Conf. ICPR2002, 2002. Google ScholarDigital Library
- Eakins, J. P. "Towards intelligent image retrieval", Pattern Recognition, Vol. 35, pp. 3--14, 2002.Google ScholarCross Ref
- El Badawy, O., El-Sakka, M., Hassanein, K. and Kamel, M. "Image data mining from financial documents based on wavelet features", Proc. IEEE ICIP-2001, Vol. 1, pp. 1078--1081, 2001.Google ScholarCross Ref
- Evgeniou, T., Pontil, M. Papageorgiou,, C. and Poggio, T. "Image representations and feature selection for multimedia database search", IEEE Trans. Knowledge and Data Engineering, Vol. 15, No. 4, pp. 911--920, 2003. Google ScholarDigital Library
- Freeman, W., Pasztor, E. and Carmicael, O. "Learning low-level vision", Int. Journal of Computer Vision, Vol. 40, No. 1, pp. 25--47, 2000. Google ScholarDigital Library
- Ghahramani, Z. "Learning Dynamic Bayesian Networks", In Adaptive Processing of Sequences and Data Structures, C. L. Giles and M. Gori (eds.), LNAI, Springer-Verlag, pp. 168--197, 1998. Google ScholarDigital Library
- Hacid, M.-S., Decleir, C. and Kouloumdjian, J. "A database approach for modeling and querying video data", IEEE Trans. Knowledge and Data Engineering, Vol. 12, No. 5, pp. 729--750, 2000. Google ScholarDigital Library
- Irani M. and Anandan, P. "Video indexing based on mosaic representation", Proc. IEEE, Vol. 86, No. 5, pp. 905--921, 1998.Google ScholarCross Ref
- Jiang, X., Munger, A. and Bunke, H. "On median graphs: properties, algorithms, and applications", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, No. 10, pp. 493--503, 2001. Google ScholarDigital Library
- Kherfi, E. L., Ziou D. and Bernardi, A. "Image retrieval from the World Wide Web: issues, techniques and systems, ACM Computing Surveys, Vol. 36, No. 1, pp. 35--67, 2004. Google ScholarDigital Library
- Laaksonen, J., Koskela, M., Laakso, S. and Oja, E. "PicSOM -- content-based image retrieval with self-organizing maps", Pattern Recognition Letters, Vol. 21, pp. 1199--1207, 2000 Google ScholarDigital Library
- Li, J. Z., Ozsu, M. T. and Szafron, D. "Modeling of moving objects in a video database", Proc. IEEE Int. Conf. Multimedia Computing and Systems, pp. 336--343, 1997. Google ScholarDigital Library
- Nascimento, M. A., Sridhar, V. and Li, X. "Region-based image retrieval using multiple-features", Journal of Visual Languages and Comp., Vol. 14, No. 2, pp. 151--179, 2003.Google ScholarCross Ref
- Oliver, N. M., Rosario, B. and Pentland, A. "A Bayesian computer vision system for modeling human interactions", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 831--843, 2000. Google ScholarDigital Library
- Ordonez C. and Omiecinski, E. "Discovering association rules based on image content", Proc. IEEE Conf. Advances in Digital Libraries, 1999. Google ScholarDigital Library
- Palenichka R. M. and Zinterhof, P. "Structure-adaptive filtering based on polynomial regression modeling of image intensity", Journal of Electronic Imaging, Vol. 10, No. 2, pp. 521--534, 2001.Google ScholarCross Ref
- Palenichka, R. M., Missaoui, R. and Zaremba, M. B. "Extraction of salient features for image retrieval using multi-scale image relevance function", Proc. Int Conf. CIVR2004, Vol. LNCS 3115, pp. 428--437, 2004.Google ScholarCross Ref
- Pan J.-Y. and Faloutsos, Ch. "VideoGraph: A new tool for video mining and visualization", Proc. First ACM+IEEE Joint Conference on Digital Libraries (JCDL 2001), 2001. Google ScholarDigital Library
- Pentland, A., Picard, R. W. and Sclaroff, A. "Photobook: content based manipulation of image databases", Int. Journal of Computer Vision, Vol. 18, no. 3, pp. 233--254, 1996. Google ScholarDigital Library
- Perner, P. Data Mining on Multimedia Data, Vol. LNCS 2558, Berlin: Springer-Verlag, 141 p., 2003. Google ScholarDigital Library
- Petrakis E. and Faloutsos, Ch. "Similarity searching in medical image databases," IEEE Trans. Knowledge and Data Eng., Vol. 9, no. 3, pp. 435--447, 1997. Google ScholarDigital Library
- Pissinou, I., Radev, K., Makki, K. and Campbell, W. J. "Spatio-temporal composition of video objects: representation and querying in video database systems", IEEE Trans. Knowledge and Data Engineering, Vol. 13, No. 6, pp. 1033--1040, 2001. Google ScholarDigital Library
- Rimey R. D. and Brown, C. M. "Control of selective perception using Bayes nets and decision theory", Int. Journal of Computer Vision, Vol. 12, pp. 173--209, 1994 Google ScholarDigital Library
- Rui, Y., Huang, T. S. and Chang, S.-F. "Image retrieval: current techniques, promising directions and open issues", Journal of Visual Communication and Image Representation, Vol. 10, No. 3, pp. 39--62, 1999.Google ScholarDigital Library
- Schaffalitzky F. and Zisserman, A. "Automated scene matching in movies", Proc. CIVR2002, LNCS 2383, pp. 186--197, 2002. Google ScholarDigital Library
- Schmid C. and Mohr, R. "Local gray-value invariants for image retrieval", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 5, pp. 530--535, 1997. Google ScholarDigital Library
- Schuldt, Ch., Laptev, I. and Caputo, B. "Recognizing human actions: a local SVM approach", Proc. Int. Conf. ICPR2004, 2004. Google ScholarDigital Library
- Sebe N. and Lew, M. S. "Comparing salient point detectors", Pattern Recognition Let., Vol. 24, No. 1--3, pp. 89--96, 2003. Google ScholarDigital Library
- Sengupta K. and Boyer, K. L. "Organizing large structural model bases", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17, No. 4, pp. 321--332, Apr. 1995. Google ScholarDigital Library
- Sheikholeslami, G., Chang, W. and Zhang, A. "SemQuery: semantic clustering and querying on heterogeneous features for visual data", IEEE Trans. Knowledge and Data Engineering, Vol. 14, No. 5, pp. 988--1002, 2002. Google ScholarDigital Library
- Smeaton, A. F. "Challenges for content-based navigation of digital video in the Fischlar digital library", Proc. CIVR2002, LNCS 2383, pp. 215--224, 2002. Google ScholarDigital Library
- Smeulders, A., Worring, M., Santini, S., Gupta, A. and Jain, R. "Content-based image retrieval at the end of the early years", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1349--1380, 2000. Google ScholarDigital Library
- Stanchev, P. "Using image mining for image retrieval", Proc. IASTED Conf. on Computer Science and Technology, Cancun, Mexico, pp. 214--218, 2003.Google Scholar
- Stauffer Ch. and Grimson, W. E. L. "Learning patterns of activity using real-time tracing", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 747--757, 2000 Google ScholarDigital Library
- Valtchev, P., Missaoui, R. and Godin, R. "Formal concept analysis for knowledge discovery and data mining: the new challenges", Proc. ICFCA 2004, pp. 352--371, 2004.Google ScholarCross Ref
- Vapnik, V. N. "An overview of statistical learning theory", IEEE Trans. Neural Networks, Vol. 10, No. 5, pp. 988--999, 1999. Google ScholarDigital Library
- Wang, W., Song, Y. and Zhang, A. "Semantic-based image retrieval by region saliency", Proc. Image and Video Retrieval, CIVR2002, Vol. LNCS 2383, pp. 29--37, 2002. Google ScholarDigital Library
- Xie, L., Chang, S.-F., Divakaran, A. and Sun, H. "Unsupervised mining of statistical temporal structures in video", In Video Mining, A. Rosenfeld, D. Doermann, and D. DeMenthon (Eds.), 2003.Google Scholar
- Zhang, J., Hsu, W. and Lee, M. L. "Image mining: issues, frameworks, and techniques", Proc. Second International Workshop on Multimedia Data Mining (MDM/KDD 2001), pp. 13--20, 2001.Google Scholar
- Zhu, X., Wu, X., Elmagarmid, A. K., Feng, Z. and Wu, L. "Video data mining: semantic indexing and event detection from the association perspective", IEEE Trans. Knowledge and Data Engineering, Vol. 17, No. 5, pp. 665--677, 2005. Google ScholarDigital Library
- Effective image and video mining: an overview of model-based approaches
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