Disaster Image Filtering and Summarization Based on Multi-layered Affinity Propagation

Abstract

In this paper, a disaster image filtering and summarization (DIFS) framework is proposed based on multi-layered affinity propagation. The proposed framework is able to automatically identify and summarize latent semantic themes (scenes) in a disaster topic and filter junk images at the same time. Specifically, the images belonging to a disaster topic are first clustered into different groups based on visual descriptors using affinity propagation (AP). Then the typical instances within each cluster are collected to perform the second-layer clustering for identifying final positive clusters by utilizing both visual and textual similarities concurrently. At both layers, the proposed curve fitting function is applied to select appropriate preference values for the AP algorithm. The experimental results on the real world Flickr data set demonstrate the effectiveness of the proposed framework.