CBVR and Classification of Video Database–Latest Trends, Methods, Effective Techniques, Problems and Challenges

Abstract

Content Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos exist. Results have shown performance improvement (higher precision and recall values) when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. In this paper a complex and wide area of CBVR and CBVR systems has been presented in a comprehensive and simple way. Processes at different stages in CBVR systems are described in a systematic way. Types of features, their combinations and their utilization methods, techniques and algorithms are also shown. Various querying methods, some of the features like GLCM, Gabor Magnitude, algorithm to obtain similarity like Kullback-Leibler distance method and Relevance Feedback Method are discussed. Functioning of Support Vector Machines (SVM) is discussed which are vital for automatic classification of videos.

Efficient visual information indexation for supporting actionable intelligence and knowledge generation

“…In a comprehensive reviews of recent advancements in the field of content-based visual indexing [1,3,4] examines various components of the CBIR process including methodologies for image and video indexing, strategies for measuring similarity among images or videos, as well as the process of search re-ranking. Content-based visual indexing using both classical and deep learning methods has been widely used to represent visual content using feature vectors.…”

Advanced Evaluation Method for Video Retrieval System

“…The capability for the manipulation and access of the stored videos is commonly utilized by the users of various domains in a variety of the ways [3,4]. The systems of video retrieval have gained great importance as a result of an increasing amount of the visual data for the videos [5]. With no efficient and effective retrieval systems, it would not be possible to cope with increases of World Wide Web and also as a result of the advancement of the digital information [6,7].…”