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 TRECVID2005. We hypothesize that the discovered patterns can reveal semantics beyond the input space, and thus evaluate the patterns against a much larger concept space containing 192 concepts defined by LSCOM. Results show that HHMM has the best average prediction among all models, however different models seem to excel in different concepts depending on the concept prior and the ontological relationship. Results also show that the majority of the target concepts are better predicted with temporal or combination hypotheses, and there are nov...

• Type: Conference
• Year: 2006
• Where: ICMCS
• Authors: Lexing Xie, Shih-Fu Chang
• Keywords: Concept Space, Hidden Markov Model, ICMCS 2006, Pattern Mining