LIREM: A Generic Framework for Effective Online Video Novelty Detection

Novelty detection in social video has drawn much attention of researchers and is applied to many tasks in real-world applications, such as e-commerce and e-learning. Existing methods cannot address this issue effectively, since most of them do not consider the quality of videos or the long-term information of online social videos. In this paper, we propose a general framework, Long-term Information REconstruction-based Model (LIREM), which cleans the video feature information and captures both short-term and long-term spatial-temporal information of video segments to detect novelty online. We first design a novel outlier detection method for feature cleaning to improve the learning performance. Then, an LSTM-Decoder model is constructed and applied to the cleaned video segments for predicting the reconstruction error of video features. Our experiments are conducted on three real datasets, and the experimental results demonstrate the performance of our model outperforms other novelty detection models.