Jonathon S. Hare

TL;DR: The proposed OCNN framework is the first object-based convolutional neural network framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images, and was tested on aerial photography of two large urban scenes in Southampton and Manchester.

TL;DR: A set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example, which achieved by far the highest classification accuracy for both LC andLU, up to around 90% accuracy, about 5% higher than the existingDeep learning methods, and 10% greater than traditional pixel-based and object-based approaches.

TL;DR: The proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination, paving the way to effectively address the complicated problem of VFSR image classification.

TL;DR: T-REx, a dataset of large scale alignments between Wikipedia abstracts and Wikidata triples, is presented, which is two orders of magnitude larger than the largest available alignments dataset and covers 2.5 times more predicates.

TL;DR: This large-scale empirical study on a set of over half a million Flickr images shows a considerable correlation between sentiment and visual features, and promising results towards estimating the polarity of sentiment in images.