Multimodal Visual Pattern Mining with Convolutional Neural Networks.

In this paper we describe a novel framework and algorithms for discovering image patch patterns from a large corpus of weakly supervised image-caption pairs generated from news events. Current pattern mining techniques attempt to find patterns that are representative and discriminative, we stipulate that our discovered patterns must also be recognizable by humans and preferably with meaningful names. We propose a new multimodal pattern mining approach that leverages the descriptive captions often accompanying news images to learn semantically meaningful image patch patterns. The mutltimodal patterns are then named using words mined from the associated image captions for each pattern. Our methods also discover named patterns beyond those covered by the existing image datasets like ImageNet. To the best of our knowledge this is the first algorithm developed to automatically mine image patch patterns that have strong semantic meaning specific to high-level news events, and then evaluate these patterns based on that criteria.