Learning from Multiple Outlooks

We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance.Comment: with full proofs of theorems and all experiment

Interpreting genesis: A note on artistic invention and the Byzantine illuminated letter

The article explores iconography of the illuminated initial letters in the Byzantine tenth century Homilies of John Chrysostom and other authors with special reference to Oxford, Bodl. lib., Auct. T. 3.3. It is argued that pictorial initials composed of human figures and human-animal combats function as detailed visual interpretations of the written text, displaying at the same time artistic uniqueness and imagination

Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN

Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection.Comment: To be presented at PatchMI: 3rd International Workshop on Patch-based Techniques in Medical Imaging, MICCAI 201