Content Based Video Retrieval Using Particle Swarm Optimization

Traditional video search engines retrieve the results on the basis of correspondence between user's textual query and tags associated with the videos. Only that content that matches the tags is returned as a result to the user. Given the ever-increasing immensity of videos on the internet, especially those with zero or irrelevant tags, such traditional methodology has eventually led to rise in ratio of missing important context. Content based searching within a video library is definitely an alternative solution but it requires time consuming computations and comparisons which renders exhaustive search unpractical. The purpose of this paper is to provide an efficient methodology that will lead to incremental improvement in the video search results against a user's query image. Our method employs Particle Swarm Optimization (PSO), an evolutionary population based search algorithm, to look for frames within the video library. The fitness of each swarm particle is the degree of similarity with respect to the content present in both the input image provided by the user and the video frame(s) fetched through PSO. This exempts us from the exhaustive and linear search of every frame of every video in the library. The relative best match in each generation of PSO is shown to the user for his engagement. For calculating the fitness of each swarm particle we have tested three similarity measures, 1) correlation based template matching, 2) score from scale-invariant feature transform (SIFT) algorithm and, 3) convolution. Preliminary results on real video library are promising.