Semantics-Biased Rapid Retrieval for Video Databases
High-dimensional index is one of the most challenging tasks for content-based video retrieved. 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, and proposed a semantics supervised cluster based index (briefly as SSCI) approach 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 onfy 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 search can be divided into two phases: 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 the approximate nearest neighbors. Our experiments showed that for approximate searching the SSCI-based approach was faster than VA+-based approach; moreover, the quality of the result set was better than that of the sequential search in terms of semantics.
Semantics video retrieval index cluster.
Zhiping Shi Qingyong Li Zhiwei Shi Zhongzhi Shi
Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100080, China Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100080, China;Graduate Schoo
国际会议
Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)
北京
英文
634-639
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）