By Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
This two-volume set (LNAI 9329 and LNAI 9330) constitutes the refereed lawsuits of the seventh overseas convention on Collective Intelligence, ICCCI 2014, held in Madrid, Spain, in September 2015.
The a hundred and ten complete papers offered have been rigorously reviewed and chosen from 186 submissions. they're geared up in topical sections comparable to multi-agent platforms; social networks and NLP; sentiment research; computational intelligence and video games; ontologies and data extraction; formal equipment and simulation; neural networks, SMT and MIS; collective intelligence in net platforms – net platforms research; computational swarm intelligence; cooperative innovations for determination making and optimization; complex networking and safety applied sciences; IT in biomedicine; collective computational intelligence in academic context; technological know-how intelligence and information research; computational intelligence in monetary markets; ensemble studying; significant information mining and searching.
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Extra info for Computational Collective Intelligence: 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II
Int. J. of Information Studies 1(3), 200–211 (2010) 19. : Advanced news video parsing via visual characteristics of anchorperson scenes. Telecommunication Systems 54(3), 247–263 (2013) 20. : Automatic fast detection of anchorperson shots in temporally aggregated TV news videos. , Kosala, R. ) ACIIDS 2015. LNCS, vol. 9012, pp. 339–348. Springer, Heidelberg (2015) 21. : Audiovisual diarization of people in video content. Multimedia Tools and Applications 68(3), 747–775 (2014) 22. : Content-based inference of hierarchical structural grammar for recurrent TV programs using multiple sequence alignment.
All other non-player shots and scenes usually recorded in a TV studio such as commentaries, interviews, charts, tables, announcements of future games, discussions of decisions of sports associations, etc. are called studio shots or studio scenes. Studio shots are not useful for video categorization and therefore should be rejected. It was observed that the studio scenes may be even two thirds of sports news. This rejection of non-player scenes before starting content analyses creates a great opportunity to significantly reduce computing time and conduct these analyses more efficiently.
The length of the intro and final animation is not constant, so after temporal segmentation at the beginning as well as at the end of a news video we can receive different sequence of shots. It is due to the fact that sometimes there is a fade from black to the intro part and the final animation usually fades away to a black frame. But we very often observed that the first frame of the intro or the last frame of the final animation are frozen, so the length of these parts of a news video are of an unpredictable duration.