| Abstract: | The number of short videos on the Internet is huge, but most of them are unlabeled. In this paper, a rough labelling method of short video based on the neural network of image classification is proposed. Convolutional auto-encoder is applied to train and learn unlabeled video frames, in order to obtain the feature in certain level of the network. Using these features, we extract key-frames of the video by our method of feature clustering. We put these key-frames which represent the video content into the image classification network, so that we can get the labels for every video clip. We also compare the different architectures of convolutional auto-encoder, while optimizing and selecting the better performance architecture through our experiment result. In addition, the video frame feature from the convolutional auto-encoder is compared with those features from other extraction methods. On the whole, this paper propose a method of image labels transferring for the realization of short video rough labelling, which can be applied to the video classes with few labeled samples. |
