关键词: 视频缩略图, 关键帧, 卷积神经网络(CNN), 循环神经网络(RNN), 视觉语义嵌入

Abstract: Video thumbnail, as the most intuitive form of video content, plays an important role in video sharing sites and is one of the key elements to attract users to click and watch the video. However, a descriptive statement related to video content with a video thumbnail associated with the content of the statement is often more attractive to user. Therefore, a complete video thumbnail recommendation framework with a deep visual-semantic embedding model is proposed in this paper. This model uses the convolutional neural network to extract the visual features of video keyframes, and uses recurrent neural network to extract the semantic features of description sentences. After embedding the visual features and the semantic features into the visual-semantic potential space of the same dimension, the key frames related to the content of the descriptive sentences are recommended as video thumbnails by comparing the correlation between the visual features and the semantic features. Experiments on different categories of web videos show that the proposed method can effectively recommend contented-related video thumbnail sequence from videos for given descriptive statements and enhance the user experience.

Key words: video thumbnail, keyframes, convolutional neural network (CNN), recurrent neural network (RNN), visual-semantic embedding