Abstract
AbstractContent-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 ...
Highlights
Content Based Image Retrieval (CBIR) is one of the technology that, principle helps to organize digital image collections by their visual content
Once a question clip is given to the Content-based Video Retrieval (CBVR) system, the clip is subjected to the feature extraction method and every one the said feature sets are extracted within the similar fashion
Visual content based retrieval of video information is a growing research area which has been in attention freshly in the middle of the researchers and experimenters
Summary
Content Based Image Retrieval (CBIR) is one of the technology that, principle helps to organize digital image collections by their visual content. The Feature Extractor may be a period system, is utilized to preprocess all the videos that square measure keep within the info and conjointly store their distinctive options for faster retrieval [9]. Histogram does not internment spatial patterns that are imperative to describe image content [17]. The frames square measure directly extracted from the video sequence; the additional machine overhead is not necessary compared with shot-based frame extraction techniques. Movement feature and object feature plays a categorically significant role in retrieving the similar video clips. For the question video clip the said options object, movement and the occlusion options square measure extracted and compared with the feature within the feature library.
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