Video Retrieval Algorithm Research Based On Bag Of Visual Words

Video has become one of main carriers of information dissemination, with the widespread use of computer networks and the popularity of digital electronic devices.Much research focuses on how to find out interest video in massive database quickly and accurately.The method called “Content-Based Video retrieval(CBVR)” becomes a hotspot.The Algorithm called “ video retrieval algorithm based on bag of visual word model” belongs to CBVR. The proposed method consists of two parts: the first part is keyframe extraction algorithm, and the second part is a weighted bag of visual word model of image construction. A two-stage frame extraction algorithm is adopted to solve the redundancy problem in the traditional keyframe extraction method. The proposed method makes good use of color and counter features, compares the similarity between image frames, selects the most representative keyframe, improves the retrieval efficiency,and constructs key frame database.Image precise query is achieved by weighted bag of visual word model based on the keyframe extraction algorithm. The model generates a high-dimensional vector to describe the image information through local feature extraction. The location of keyframe information is obtained by calculating the similarity query image and keyframe image. The algorithm can automatically extract and describes the features and video content without human intervention. The algorithm can achieve video retrieval implementation independently.Video keyframe extraction and keyframes database building are used to test the retrieval performance of the algorithm. Experimental results verify that the algorithm can implement video search independently, and achieve accurate and objective retrieval. It has a high recall and precision.