Cluster-based Index Method for Video Database
Shi Zhi · Chinese Journal of Computers · 2007
A perfect video database organization should be that video feature vectors,which are not only semantic relevant but also their visual features themselves similar,are stored continuously.According to large-scale video database character,the authors hierarchically cluster feature vectors in video database supervised by video semantic classes until every cluster just contains such videos that belong to the same semantic class.The clusters here are called as index clusters.An index entry is created for an index cluster and a Bayes classifier is built with probability relationship between low-level features and the semantic class.For a given query,the first phase computes the distances between the query example and each cluster index and returns the clusters with the smallest distance,here namely candidate clusters;then the second phase retrieves the original feature vectors within the candidate clusters to gain uhe approximate nearest neighbors.The proposed method speeds up searching and improves retrieval semantic sensitivity.