Multimodal Content-Aware Image Thumbnailing

Kohei Yamamoto, Hayato Kobayashi, Yukihiro Tagami, Hideki Nakayama

Abstract: News article recommendation has the key problem of needing to eliminate the redundant information in a ranked list in order to provide more relevant information within a limited time and space. In this study, we tackle this problem by using image thumbnailing, which can be regarded as the summarization of news images. We propose a multimodal image thumbnailing method considering news text as well as images themselves. We evaluate this approach on a real data set based on news articles that appeared on Yahoo! JAPAN. Experimental results demonstrate the effectiveness of our proposed method.