Dynamic Batch Nearest Neighbor Search in Video Retrieval

To retrieve similar database videos to a query clip, each video is typically represented by a sequence of highdimensional feature vectors. Given a query video containing m feature vectors, an independent Nearest Neighbor (NN) search for each feature vector is often first performed. Completing all the NN searches, an overall similarity is then computed, i.e., a single video retrieval usually involves the searches for m times. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch Nearest Neighbor (BNN) search is stated as a single operation that performs a batch of individual NN searches. This paper presents a novel approach to efficient high-dimensional BNN search called Dynamic Query Ordering (DQO) for advanced optimizations in both I/O and CPU cost. Observing the overlapped candidates (or search space) of a pervious query may help to ...

• Type: Conference
• Year: 2007
• Where: ICDE
• Authors: Jie Shao, Zi Huang, Heng Tao Shen, Xiaofang Zhou, Yijun Li
• Keywords: Database, ICDE 2007, Overall Query Performance, Pervious Query, Query Video