Guide: Dr. Ponnurangam Kumaraguru, Dr. AV Subramanyam
Team Size: 2
Team Member(s): Sonal Goel
Time Period: May'16-July'16
Technologies/Concepts Used: Matlab, Discriminative Learning, Object Recognition, Filtering Social Media Datasets
In this paper we propose a methodology for visual event summarization by extracting mid-level visual elements from images associated with social media events on Twitter (#VisualHashtags). The key research question is Which elements can visually capture the essence of a viral event? hence explain its virality, and summarize it. Compared to the existing approaches of visual event summarization on social media data, we aim to discover #VisualHashtags, i.e., meaningful patches that can become the visual analog of a regular text hashtag that Twitter generates. Our algorithm incorporates a multi-stage filtering process and social popularity based ranking to discover mid-level visual elements, which overcomes the challenges faced by direct application of the existing methods.