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Pattern Mining in Visual Concept Streams

Xie, Lexing; Chang, Shih-Fu

Description

Pattern mining algorithms are often much easier applied than quantitatively assessed. In this paper we address the pattern evaluation problem by looking at both the capability of models and the difficulty of target concepts. We use four different data mining models: frequent itemset mining, k-means clustering, hidden Markov model, and hierarchical hidden Markov model to mine 39 concept streams from the a 137-video broadcast news collection from TRECVID-2005. We hypothesize that the discovered...[Show more]

CollectionsANU Research Publications
Date published: 2006
Type: Conference paper
URI: http://hdl.handle.net/1885/58059
Source: Proceedings IEEE International Conference on Multimedia and Expo (ICME 2006)
DOI: 10.1109/ICME.2006.262457

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