140 research outputs found
Automatic video annotation with forests of fuzzy decision trees
Nowadays, the annotation of videos with high-level semantic concepts or
features is a great challenge. In this paper, this problem is tackled by learning,
by means of Fuzzy Decision Trees (FDT), automatic rules based on a limited
set of examples. Rules intended, in an exploitation step, to reduce the need of
human usage in the process of indexation. However, when addressing large,
unbalanced, multiclass example sets, a single classi er - such as the FDT -
is insu cient. Therefore we introduce the use of forests of fuzzy decision
trees (FFDT) and we highlight: (a) its e ectiveness on a high level feature
detection task, compared to other competitive systems and (b) the e ect on
performance from the number of classi ers point of view. Moreover, since the
resulting indexes are, by their nature, to be used in a retrieval application, we
discuss the results under the lights of a ranking (vs. a classi cation) context.Peer Reviewe
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Minin
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