Research on emotional semantic retrieval of attention mechanism oriented to audio-visual synesthesia

Digital video is widely used to record people's daily lives and share people's moods, but few researchers have conducted research on the consistency of emotional expression between short videos and music. In order to be able to match the appropriate background music to the short video image autonomously and efficiently, the paper analyzed the emotional connection between the two from the audio-visual synes-thesia. First, emotional semantics was used as a bridge to connect video data and music data, and a video -music synesthesia data set based on semantic words was constructed. Then, an attention mechanism was incorporated to better extract key features in video images. In the extraction of music features, an improved lenet5 network was used, and the optimal network parameters were determined through experiments. Finally, the two types of features were fused and the mutual retrieval between video and music was performed. In order to compare the performance of different models, different CNN models were calculated in the processing of video images, including VGG16, VGG19, AlexNet and GoogleNet, and the attention mechanism was added to each network for calculation to compare its retrieval accu-racy. In the processing of music data, different CNN algorithms were also used for comparative experi-ments, and networks with different layers were used to determine the optimal results. The experimental results show that the audiovisual synesthesia retrieval model based on emotion can effec-tively measure the emotional similarity between video images and music, and the method of the paper can produce a good match between them. The research method of the paper is the exploration of com-puter synesthetic intelligence, which can stimulate the creative inspiration of image and music creative designers. While enhancing the emotional experience of digital products, it also improves the efficiency and quality of development. (c) 2022 Elsevier B.V. All rights reserved.