An effective content based video retrieval utilizing texture, color and optimal key frame features

Video retrieval is an important technology used in the design of video search engines and extraction of a preliminary set of related videos from the database. The necessity of efficiently querying generally available video data has improved with the increase in the availability of huge quantities of such data. Hence, content-based video data retrieval proves to be a challenging and crucial problem. In this paper, with the intention of retrieving video for a given query, the raw video data is represented by two different representation schemes, video segment representation (VSR) and Optimal key frame representation (OFR) based on the visual contents. At first, the input raw video is segmented using video object segmentation algorithm so that the objects presented in this raw video can be obtained. Then, feature vectors are computed from VSR using the texture analysis and color moments. Furthermore, the optical frame (OFR) is extracted by considering the probability of occurrence of the pixel intensity values with respect to the pixel location among every frame presented in a raw video. Finally, all these features of a video, texture, color and optical frame are combined as a feature set and stored in the feature library. For the query video clip, the aforesaid features are extracted and compared with the feature in the feature library. The comparison is achieved via the feature weighted distance measure and the similar videos are retrieved from the collection of videos. The experimentation is carried out on the videos in the open video project and experimental results ensured that the proposed video retrieval scheme achieves a better performance level for video retrieval.