Pattern Mining in Visual Concept Streams
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]
|Collections||ANU Research Publications|
|Source:||Proceedings IEEE International Conference on Multimedia and Expo (ICME 2006)|
|01_Xie_Pattern_Mining_in_Visual_2006.pdf||431.76 kB||Adobe PDF||Request a copy|
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