Near-Duplicate Segments based news web video event mining

News web videos uploaded by general users usually include lots of post-processing effects (editing, inserted logo, etc.), which bring noise and affect the similarity comparison for news web video event mining. In this paper, a framework based on the concept of Near-Duplicate Segments (NDSs) which effectively integrates spatial and temporal information is proposed. After each video being divided into segments, those segments from different videos but sharing similar visual content are clustered into groups. Each group is named as an NDS, which infers the latent content relations among videos. The spatial-temporal local features are extracted and used to represent each video segment, which could effectively capture the main content of news web videos and omit the noise such as the disturbance/influence from video editing. Finally, the visual information is integrated with the textual information. The experiment demonstrates that our proposed framework is more effective than several existing methods with a significant improvement.