Semantics Supervised Cluster-Based Index for Video Databases

High-dimensional index is one of the most challenging tasks for content-based video retrieval (CBVR). Typically, in video database, there exist two kinds of clues for query: visual features and semantic classes. In this paper, we modeled the relationship between semantic classes and visual feature distributions of data set with the Gaussian mixture model (GMM), and proposed a semantics supervised cluster based index approach (briefly as SSCI) to integrate the advantages of both semantic classes and visual features. The entire data set is divided hierarchically by a modified clustering technique into many clusters until the objects within a cluster are not only close in the visual feature space but also within the same semantic class, and then an index entry including semantic clue and visual feature clue is built for each cluster. Especially, the visual feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two ...

• Type: Conference
• Year: 2006
• Where: CIVR
• Authors: Zhiping Shi, Qingyong Li, Zhiwei Shi, Zhongzhi Shi
• Keywords: CIVR 2006, Image Analysis, Semantic Classes, Visual Feature, Visual Feature Distributions