A Gram-Based String Paradigm for Efficient Video Subsequence Search

Zi Huang, Jiajun Liu, Bin Cui, Xiaoyong Du
2013 IEEE transactions on multimedia  
The unprecedented increase in the generation and dissemination of video data has created an urgent demand for the large-scale video content management system to quickly retrieve videos of users' interests. Traditionally, video sequence data are managed by high-dimensional indexing structures, most of which suffer from the well-known "curse of dimensionality" and lack of support of subsequence retrieval. Inspired by the high efficiency of string indexing methods, in this paper, we present a
more » ... g paradigm called VideoGram for large-scale video sequence indexing to achieve fast similarity search. In VideoGram, the feature space is modeled as a set of visual words. Each database video sequence is mapped into a string. A gram-based indexing structure is then built to tackle the effect of the "curse of dimensionality" and support video subsequence matching. Given a high-dimensional query video sequence, retrieval is performed by transforming the query into a string and then searching the matched strings from the index structure. By doing so, expensive high-dimensional similarity computations can be completely avoided. An efficient sequence search algorithm with upper bound pruning power is also presented. We conduct an extensive performance study on real-life video collections to validate the novelties of our proposal. Index Terms-High-dimensional indexing, sequence indexing, similarity search, video subsequence search.
doi:10.1109/tmm.2012.2236307 fatcat:tcopeotgkjd3rayabxpqoaxgw4