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Mining Video Association Rules Based on Weighted Temporal Concepts

Discovery of video association rules has been found useful in many applications to explore the video knowledge such as video indexing, summarization, classification and semantic event detection. The traditional classical association rule mining algorithms can not apply directly to the video database. It differs in two ways such as spatial and temporal properties of the video database and significance of the items in the vide cluster sequence. The proposed paper discovers significant relationships in video sequence using weighted temporal concepts. The weights of the video items take the quality of transactions into considerations using modified link-based models. The proposed Modified HITS based weighted temporal concept did not require pre-assigned weights. The mined association rules have more practical significance. This strategy identifies the valuable rules comparing with Apriori based video sequence algorithm. We also present results of applying these algorithms to a synthetic data