Feature mining simulation of video image information in multimedia learning environment based on BOW algorithm

摘要

With the development of computer image processing technology, video image information feature mining in multimedia environment has become a research hotspot in the field of education with its unique characteristics. However, the current method is to classify and retrieve through the video image information features. When the video image information is disordered, the multimedia video image information features cannot be classified, and the criterion for measuring the size of the video image information cannot be given, and the information feature mining accuracy is low. Therefore, this paper proposes a new efficient algorithm for multimedia video image information retrieval. Firstly, the SIFT features of the video image are analysed to obtain the SIFT features of the video image. Secondly, the SIFT feature is used for feature matching to identify the target image. Finally, the BOW algorithm is introduced to index the matched SIFT features, and the bag-of-words model, TF-IDF weighting and Euclidean distance are used to complete the similarity calculation of the image, and the feature mining of multimedia video image information is completed. The simulation results show that the proposed method effectively improves the feature mining speed and feature mining accuracy and has better robustness.