Dimensionality reduction for fast and accurate video search and retrieval in a large scale database

A large amount of video data is generated every day. Searching through huge video database is an important problem in many applications. For instance, individuals may want to search for video content they are interested in from YouTube video, media companies may want to locate video content that violates their copyright protection and so on. Fast and accurate algorithm in all these cases is needed for efficient video retrieval. The high dimensionality of video sequence poses a major challenge of video indexing and retrieval. As dimensionality increases, query performance degrades. This phenomenon generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by loss of precision of query results. Feature extraction and dimensionality reduction are highly related to each other, as the combined goal of the two processing steps is to generate a compact representation of the content of an image. Here we propose to perform dimensionality reduction in both phases of the video search and retrieval system by extracting appropriate features. We shall try to exploit the use of principal component analysis to transform the original data of high dimensionality into new co-ordinate system with low dimensionality and then use sparse representation before applying similarity match for fast and accurate search and retrieval of videos.