A Review on Content-Based Image Retrieval from Videos using Self Learning Object Dictionary

Content-based video retrieval is very interesting to the point where it can be used in real-world. Video retrieval is regarded as one of the most important in multimedia research. There are different types of representations for video i. e. low level representations and high level representations having different features. Video retrieval can be used for multiuser systems for video search and browsing which are useful in web applications. The project takes the information needs and retrieval data already present in the archive, and that retrieval performance can be significantly improved when content-based image retrieval (CBIR) algorithm are applied to search. With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems, as users shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval regardless of video attributes being under consideration. Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. This survey reviews the interesting features that can be extracted from video data for indexing and retrieval along with similarity measurement methods. We also identify present research issues in area of content based video retrieval systems.