Multimodal Event Detection and Summarization in Large Scale Image Collections

This paper describes a multimodal graph-based approach to address the problem of event detection and summarization in large scale image collections. A first version of our system was presented in the Yahoo-Flickr Event Summarization Challenge of ACM Multimedia 2015 [6]. The objective of the approach is to automatically detect events within millions of photos and summarizing them efficiently for user consumption. The presented approach uses a moving time window over the collection of multimedia items to build a same-event image graph and applies graph clustering to detect events. In addition, it makes use of a graph-based diversity-oriented ranking algorithm to summarize instances of the detected events. A demo of the system is online at: http://mklab.iti.gr/acmmm2015-gc/.