By Chengjun Zhu, Yuanxin Ouyang, Lei Gao, Zhenyong Chen, Zhang Xiong (auth.), Ernesto Damiani, Kokou Yetongnon, Richard Chbeir, Albert Dipanda (eds.)
This publication constitutes the completely refereed post-conference complaints of the second one overseas convention on on Signal-Image expertise and Internet-Based structures, SITIS 2006, held in Hammamet, Tunisia, in December, 2006.
The 33 complete papers have been rigorously reviewed and chosen from the easiest papers provided on the convention and are provided in revised and prolonged shape. a part of the papers specialize in the rising modeling, illustration and retrieval ideas that keep in mind the quantity, style and variety of knowledge available in dispensed computing surroundings. different contributions are dedicated to rising and novel strategies, architectures and methodologies for developing an interconnected international during which info should be exchanged simply, initiatives will be processed collaboratively, and groups of clients with equally pursuits might be shaped whereas addressing protection threats which are current greater than ever before.
Read or Download Advanced Internet Based Systems and Applications: Second International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2006, Hammamet, Tunisia, December 17-21, 2006, Revised Selected Papers PDF
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Extra resources for Advanced Internet Based Systems and Applications: Second International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2006, Hammamet, Tunisia, December 17-21, 2006, Revised Selected Papers
3 The Programme Unit Fingerprints A ﬁngerprint is a representation that univocally characterises a given object. The concept has been recently adopted in many areas, from bioinformatics [13,18,19] to various multimedia applications [9,23,25]. We deﬁne our ﬁngerprints as vectors in a fuzzy space. The ﬁngerprint construction can be seen as a space transformation process, which maps each Programme Unit description from the surrogate’s vector space to a new fuzzy vector space, whose dimensions are the number of clusters in the database, and whose elements are (fuzzy) degrees of membership of the Programme Unit to the corresponding cluster.
Messina, M. L. Sapino Table 1. Mean and Standard Deviation (SD) values for the average shot length (ASL) distributions obtained for each of the semantic classes in the database News Commercials Cartoons Football Music W. F. 87 Programme Units Clustering. The fuzzy C-means algorithm is run on the set of Programme Units of the learning data set (the entire database), restricted to a speciﬁc sub-view of each of the four views in the features vector. t. that subview. The number of clusters (and the size of the ﬁngerprint) can be reduced, by a cluster ﬁltering algorithm .
The amount of not included shots that are temporally internal to the cluster extension), (iv) the shot clusters distribution properties (which describe how the shot clusters are distributed along the Programme Unit timeline), and (v) the shot clusters relationships properties, which capture the mutual temporal relationships (such as adjacency, concatenation, inclusion) among clusters. The structural properties of a Programme Unit u are represented by the vector Su = [s1 , . . , s5 ] , in which each subvector si is associated with one of the ﬁve classes of structural properties described above.