: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
Abstract. We present a novel model for object recognition and detection that follows the widely adopted assumption that objects in images can be represented as a set of loosely cou...
Thomas Deselaers, Andre Hegerath, Daniel Keysers, ...
We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first a...
A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a si...
Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukt...
We propose a novel general framework with a boosting algorithm to achieve active object classification by view selection. The proposed framework actively decides the next best vie...