By Nadia Bianchi-Berthouze, Tomofumi Hayashi (auth.), Osmar R. Zaïane, Simeon J. Simoff, Chabane Djeraba (eds.)
1 WorkshopTheme electronic multimedia di?ers from earlier kinds of mixed media in that the bits that characterize textual content, photos, animations, and audio, video and different signs will be taken care of as info by way of computing device courses. One part of this assorted facts in termsofunderlyingmodelsandformatsisthatitissynchronizedandintegrated, consequently it may be taken care of as necessary information documents. Such documents are available in a few parts of human endeavour. glossy medication generates large quantities of such electronic facts. one other - abundant is architectural layout and the similar structure, engineering and c- struction (AEC) undefined. digital groups (in the wide feel of this observe, such as any groups mediated by means of electronic applied sciences) are one other instance the place generated facts constitutes an necessary facts checklist. Such info may well comprise info approximately member pro?les, the content material generated through the digital group, and verbal exchange info in di?erent codecs, together with email, chat files, SMS messages, videoconferencing documents. now not all multimedia info is so diversified. An instance of much less varied info, yet facts that's higher when it comes to the accumulated volume, is that generated via video surveillance platforms, the place each one quintessential information checklist approximately includes a collection of time-stamped pictures – the video frames. as a minimum, the gathering of such in- gral information files constitutes a multimedia information set. The problem of extracting significant styles from such information units has ended in the examine and devel- ment within the quarter of multimedia information mining.
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Additional info for Mining Multimedia and Complex Data: KDD Workshop MDM/KDD 2002. PAKDD Workshop KDMCD 2002. Revised Papers
17. J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer. Multi-camera multi-person tacking for easyliving. In Proc. of 3rd IEEE International Workshop on Visual Surveillance, pages 3–10, 2000. 18. S. Shafer, J. Krumm, B. Meyers, B. Brumitt, M. Czerwinski, and D. Robbins. The new easyliving project at microsoft research. In Proc. of DARPA/NIST Workshop on Smart Spaces, pages 127–130, 1998. 19. M. Coen. The future of human-computer interaction on how I learned to stop worrying and love my intelligent room.
Each pixel value for a T M M I can be computed as follows after it is scaled up or down if we assume that T M M I is a 256 gray scale image. Each P ixel V alue = 256 − Corresponding M atrix V alue Figure 4 shows some visualization examples of AM M I, SC and SR such that how these SC and SR can capture where the motions occur. Two SRs in Figure 4 (a) are same, which means that the vertical locations of two motions are same. Similarly, Figure 4 (b) shows that the horizontal locations of two motions are same by SCs.
Of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1999.  C. J. Swain and V. Athitsos, “WebSeer: An Image Search Engine for the World Wide Web,” University of Chicago Technical Report TR-96-14, July 31, 1996.  Y. Gong and H. J. Zhang, “An Effective Method for Detecting Regions of Given Colors and the Features of the Region Surfaces”, in Proc. of Symposium on Electronic Imaging Science and Technology: Image and Video Processing II, pp. 274–285, San Jose, CA, February 1994, IS&T/SPIE.