By A. Rizzi, C. Gatta, C. Slanzi, G. Ciocca (auth.), Stéphane Bres, Robert Laurini (eds.)

ISBN-10: 3540304886

ISBN-13: 9783540304883

ISBN-10: 3540323392

ISBN-13: 9783540323396

Visual info structures at the circulation Following the good fortune of earlier foreign meetings of visible details structures held in Melbourne, San Diego, Amsterdam, Lyon, Taiwan, Miami, and San Francisco, the eighth foreign convention on visible info structures held in Amsterdam handled numerous points, from visible structures of multimedia info, to platforms of visible info reminiscent of photo databases. dealing with of visible details is boosted by means of the speedy bring up of and web functions. Now, advances in sensors have grew to become all types of data into electronic shape. expertise for visible details platforms is extra urgently wanted than ever ahead of. what's wanted are new computational how to index, compress, retrieve and become aware of pictorial info, new algorithms for the archival of and entry to large quantities of electronic photos and movies, and new structures with pleasant visible interfaces. visible details processing, gains extraction and aggregation at semantic point and content-based retrieval, and the examine of person purpose in question processing will stay components of serious curiosity. As electronic content material turns into common, problems with supply and intake of multimedia content material have been additionally themes of this workshop. Be at the flow… June 2005 Stéphane Bres Robert Laurini

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Additional info for Visual Information and Information Systems: 8th International Conference, VISUAL 2005, Amsterdam, The Netherlands, July 5, 2005, Revised Selected Papers

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3. Expert Voting. Recognition is performed using a rule combining the outputs of the three experts. According to the sum-rule, a similarity measure s(T, c) is obtained by adding the scores obtained from each expert: 3 sj (T, c) s(T, c) = (12) j=1 Let us call c1 the class obtaining the highest score. The identity assigned to the query image T is c1 . If we consider that class c2 performs the second highest score, the following confidence measure b(T, c1 ) is computed: b(T, c1 ) = log s(T, c1 ) s(T, c2 ) (13) 30 4 M.

Which are effectively weaken in the result by our method. We also compare the colorization time between the method of Levin et al. and ours. 0. The results are shown in Figure7, in which X-axis refers to the test image index and Y-axis refers to the process time. 80G and 512MB memory. Statistically, the colorization time by the method of Levin et al. is about 3~4 times of our method. For the last few images, the colorization time by the method of Levin et al. increase greatly, mainly because the memory storage is out and the disk storage is used in their colorization process.

We consider the 4 previous images for each image to create the accumulative difference image. A counter for each pixel location in the accumulative image is incremented every time a difference bigger than a threshold occurs at that pixel location between the present image and one of the 4 previous images. Thus there is an accumulative image difference for each image in such a way that the entry in a given pixel in the image difference represents how many times the luminance value in this position in the 4 previous images is different from the luminance value in the present image.

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Visual Information and Information Systems: 8th International Conference, VISUAL 2005, Amsterdam, The Netherlands, July 5, 2005, Revised Selected Papers by A. Rizzi, C. Gatta, C. Slanzi, G. Ciocca (auth.), Stéphane Bres, Robert Laurini (eds.)


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