By Arnold W. M. Smeulders (auth.), Nicu Sebe, Yuncai Liu, Yueting Zhuang, Thomas S. Huang (eds.)
Welcome to the foreign Workshop on Multimedia content material research and Mining, MCAM 2007. Our workshop provides a photo of the present wor- extensive learn in multimedia research. via contemporary advances in computing, networking, and information garage, multimedia will create new attention-grabbing technical probabilities in a variety of ?elds, resembling leisure, trade, technology, medication, and public security. To bene?t from this capability, builders want - accountable innovations for the research, seek, and administration of multimedia info, in addition to disbursed process architectures within which those innovations will be embedded to e?ectively support the clients. lately, there were many workshops and conferences devoted to M- timedia, yet such a lot of them the traditional development consisting normally of oral and poster shows. all of us consider that there's a desire for a gathering that truly is a workshop, i.e., person who offers loads of possibilities for discussions and dissemination. for that reason, the structure of the workshop contains four p- els on sizzling issues in multimedia followedby discussions and poster displays. the themes of the panels are: Multimedia research and purposes, Multi- dia seek and Mining, P2P Streaming, and safeguard. This quantity encompasses a variety of invited contributions from specialists within the sector in addition to the chosen ordinary contributions. This yr 139 submissions from thirteen international locations have been submitted and forty six have been authorised for presentation on the convention after being reviewed via this system Committee members.
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Extra resources for Multimedia Content Analysis and Mining: International Workshop, MCAM 2007, Weihai, China, June 30-July 1, 2007. Proceedings
Semantic concepts of video objects were derived from color clustering and reasoning. In  the authors have presented video annotation and retrieval based on high-level concepts derived from machine learned concept detectors that exploit low level visual features. The ontology includes both semantic descriptions and structure of concepts and their lexical relationships, obtained from WordNet. Multimedia Ontology Based Computational Framework 21 Other researchers have proposed integrated multimedia ontologies that incorporate both linguistic terms and visual or auditory data and their descriptors.
Vol. 1, pp. 1174–1181 (2005) 16. : Large-scale concept ontology for multimedia. IEEE Multimedia 13(3), 86–91 (2006) 17. : In: Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Reading (1990) 18. : Knowledge representation for semantic multimedia content analysis and reasoning. In: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (November 2004) 19. : Towards bridging the semantic gap in multimedia annotation and retrieval.
In PeerCast, peers are organized into multicast trees for delivering data, with each data packet being disseminated using the same structure. When a peer receives a data packet, it also forwards copies of the packet to each of its children. Since all data packets follow this structure, it becomes critical to ensure the structure is optimized to offer good performance to all receivers. Generally, there exist four route selection strategies in tree-based topology: random selection, round-robin selection, smart selection according to physical placement, and smart selection according to bandwidth.