Convolutional neural networks for disaster images retrieval

This paper presents the method proposed by MRLDCSE team for the disaster image retrieval task in Mediaeval 2017 challenge on Multimedia and Satellite. In the proposed work, for visual information, we rely on Convolutional Neural Networks (CNN) features extracted with two different models pre-trained on ImageNet and places datasets. Moreover, a late fusion technique is employed to jointly utilize visual and the additional information available in the form of meta-data for the retrieval of disaster images from social media. The average precision for our three different runs with visual information only, meta-data and combination of meta-data and visual information are 95.73%, 18.23% and 92.55%, respectively.