Towards Effective Indexing for Very Large Video Sequence Database

With rapid advances in video processing technologies and ever fast increments in network bandwidth, the popularity of video content publishing and sharing has made similarity search an indispensable operation to retrieve videos of user interests. The video similarity is usually measured by the percentage of similar frames shared by two video sequences, and each frame is typically represented as a highdimensional feature vector. Unfortunately, high complexity of video content has posed the following major challenges for fast retrieval: (a) effective and compact video representations, (b) efficient similarity measurements, and (c) efficient indexing on the compact representations. In this paper, we propose a number of methods to achieve fast similarity search for very large video database. First, each video sequence is summarized into a small number of clusters, each of which contains similar frames and is represented by a novel compact model called Video Triplet (ViTri). ViTri models a...

• Type: Conference
• Year: 2005
• Where: SIGMOD
• Authors: Heng Tao Shen, Beng Chin Ooi, Xiaofang Zhou
• Keywords: Database, Efficient Similarity Measurements, Fast Similarity Search, SIGMOD 2005, Video Similarity Measure