A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...
Most object detection techniques discussed in the literature are based solely on texture-based features that capture the global or local appearance of an object. While results indi...
We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotati...
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...