We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how...
In the context of large databases, data preparation takes a greater importance : instances and explanatory attributes have to be carefully selected. In supervised learning, instanc...
We present novel intelligent tools for mining 3D medical images. We focus on detecting discriminative Regions of Interest (ROIs) and mining associations between their spatial distr...
Abstract. We propose a novel framework, aspect space, to balance deformability and discriminability, which are often two competing factors in shape and image representations. In th...
— Context is critical for reducing the uncertainty in object detection. However, context modelling is challenging because there are often many different types of contextual infor...