Abstract
Automatic summarization of streaming news images is critical for efficient news browsing. Although image duplicates are redundant for news reading, the number of duplicates of a news image is a good indicator for its importance. We describe the architecture used in a news aggregation system for online streaming news image summarization. Given a sequence of images for a news topic, we first cluster image duplicates based on a two-stage feature matching process, followed by representative image selection inside each cluster. Images with a high importance score are ranked chronologically to generate a timeline summarization. Our timeline summarization is not limited to a fixed size but enables users to zoom in to see more images with more details based on their interests. Experiments on real-world news data demonstrate that the timelines produced by our method can generate accurate and dynamic timeline summarizations.
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