Label Distribution Learning for Video Summarization

There is a problem of complicated model training in the supervised video digest algorithm.To solve this problem,a new video summary algorithm based on label distribution learning (LDL) is proposed.This algorithm uses non-parametric supervised learning to generate summarization.The main idea is to transfer summary structures from the annotated video to the same type of test video by label passing.Firstly,the convolutional neural network features and color features of the video are extracted.A feature matrix is obtained by combining these two features and reducing the dimension.It is then entered into the LDL model along with the label distribution of the training samples.Finally,the key frames are selected according to the label distribution of the model output,and they are composed into a video summary.By comparing the experiments with other algorithms on the benchmarks,it shows that the summaries generated by this algorithm are highly consistent with the human- created abstract,which is obviously superior to other methods.