A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

Abstract

Content-based video retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based video retrieval, feature combination plays a key role. As a result content-based retrieval of all different type video data turns out to be a challenging and vigorous problem. This paper presents an effective content based video retrieval system, which recognizes and retrieves videos with three different types of visual effects. The raw video information is divided into shots and also the object feature, movement feature and also the occlusion options are extracted from these shots and also the feature library is used for the storage method of those options. Advanced on, the Kullback-Leibler distance is computed among the options of the feature library and also the options of the question clip that's extracted within the similar manner. The results show that it is possible to improve a system for content-based video retrieval by using Kullback-Leibler distance model, which takes careful consideration of the structure and distribution of visual features. Hence the final results with the aid of the Kullback-Leibler distance the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.

A distributed Content-Based Video Retrieval system for large datasets

“…Author details 1 Department of Mathematics and Informatics, Faculty of Sciences, Hassan II University, Casablanca, Morocco. 2 Faculty of Sciences, Hassan II University, Casablanca, Morocco.…”

“…Therefore, it seems crucial to have automatic video content analysis systems capable of representing, modeling, indexing, retrieving, browsing, or searching information stored in large multimedia databases. These techniques are grouped into a single concept of Content-Based Video Retrieval systems (CBVR) [1,2].Various CBVR approaches based on low-level features are developed. For example [3] uses histogram-based color descriptors [4,5] use motion and color information [6,7]…”

“…Therefore, it seems crucial to have automatic video content analysis systems capable of representing, modeling, indexing, retrieving, browsing, or searching information stored in large multimedia databases. These techniques are grouped into a single concept of Content-Based Video Retrieval systems (CBVR) [1,2].…”

Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System

“…It has been difficult and time-consuming to conduct research into video processing because of these characteristics. Multimedia retrieval is utilized in a variety of multimedia software, applications, and processing, and it helps users find videos, images and sounds relevant to their interests [5,6]. Unstructured and large video data present many problems that need to be addressed when it comes to management, processing, and archiving [7].…”