Research On Content-based Video Retrieval Technology

With the rapid development of Internet information technology and multimedia technology,people will be exposed to more and more multimedia information in their lives.How to effectively search in the vast video data is a very important research topic.At present,researchers generally use text-based retrieval methods to use content-based retrieval methods,greatly improving retrieval efficiency and accuracy.This paper aims to optimize the key aspects of content-based video retrieval and realize a video retrieval system with high detection accuracy,which facilitates the retrieval of video and video.The main research contents of this paper are as follows:Firstly,feature extraction is performed on the image: the color feature vector of the image is obtained by using the color histogram to quantize the HSV color space,the texture feature vector of the image is obtained by using the LBP operator,and the feature vector distance is measured by the Euclidean distance method;Secondly,the color and texture features are fused by a fixed weight,and the image frame difference is calculated by dividing and weighting the video image frame.After introducing various lens boundary detection methods,the adaptive high-low threshold calculation method is designed for this paper,and the lens mutation and lens gradient are judged respectively.At the same time,the second judgment method is designed to accurately identify the lens mutation and gradient.And exclude the effects of special cases on shot boundary detection.According to the comparison experiment results,the lens boundary detection method of this paper has higher precision and recall rate.Thirdly,the method of extracting various key frames is described,and it is determined that K-means clustering method is adopted to realize key frame extraction.After analyzing the shortcomings of K-means clustering method,a key frame extraction algorithm based on K-means clustering improvement is proposed.The algorithm selects the initial class core according to the principle that the distance between the initial cluster centers is as far as possible.Through the "elbow" shape K value screening and CH index evaluation to confirm the optimal K value,the improvement of the traditional K-means clustering method is completed,and the video key frame is extracted by using this method.After comparative experiments,the method of this paper does have a better effect on the extraction of key frames.Try to use a more concise key frame number to accurately describe the lens content.Finally,based on genetic algorithm,a method based on genetic algorithm for automaticselection of multi-feature weights is designed.More reasonable color and texture feature vector weights are selected for feature fusion to improve retrieval accuracy.Using the method described in each chapter of this paper,a complete video retrieval system is constructed.After testing,the system has a good video retrieval effect and has a high retrieval accuracy.