Sequence matching using a spatio-temporal wavelet decomposition

Indexing and retrieval of image sequences are fundamental steps in video editing and film analysis. Correlation-based matching methods are known to be very expensive when used with large amounts of data. As the size of sequence database grows, traditional retrieval methods fail. Exhaustive search quickly breaks down as an efficient strategy for sequence databases. Moreover, traditional indexing with labels has a lot of drawbacks since it requires a human intervention. New advanced correlation filters are being proposed so as to decrease the computational load of the task.(10,11) A new method for retrieval of images sequences in large database based on a spatio-temporal wavelet decomposition is proposed here. It will be shown how the use of the multiresolution approach can lead to good results in terms of computationally efficiency and robustness to noise. We will assume that the query sequence may not be contained in Me database for different reasons: the presence of a noise signal on the query (e.g. due to a lossy compression scheme), or different digitization process (e.g. a different sampling rate), or the query is only similar to sequences in the database. As a consequence we are providing have developed a new efficient retrieval strategy that analyzes the database in order to extract the most similar sequences to a given query. The wavelet transform has been chosen as the framework to implement the multiresolution, formalism, because of its good compression capabilities, especially for embedded schemes. and the good features it provides for signal analysis. This paper describes the principles of a multiresolution sequence matching strategy and outlines its performance through a series of experimental simulations.