Content Based Video Retrieval using trajectory and Velocity features

The Internet forms today’s largest source of Information containing a high density of multimedia objects and its content is often semantically related. The identification of relevant media objects in such a vast collection poses a major problem that is studied in the area of multimedia information retrieval. Before the emergence of content-based retrieval, media was annotated with text, allowing the media to be accessed by text-based searching based on the classification of subject or semantics.In typical content-based retrieval systems, the contents of the media in the database are extracted and described by multi-dimensional feature vectors, also called descriptors. In our paper to retrieve desired data, users submit query examples to the retrieval system. The system then represents these examples with feature vectors. The distances (i.e.,similarities) between the feature vectors of the query example and those of the media in the feature dataset are then computed and ranked. Retrieval is conducted by applying an indexing scheme to provide an efficient way to search the video database. Finally, the system ranks the search results and then returns the top search results that are most similar to the query examples.Therefore, a content-based retrieval system has four aspects: feature extraction and representation, dimension reduction of feature, indexing, and query specifications. With the search engine being developed, the user should have the ability to initiate a retrieval procedure by using video retrieval in a way that there is a better chance for a user to find the desired content.