Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering




+44 (0)20 7594 6173c.ciliberto CV




1003Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Ciliberto, C and Fanello, SR and Santoro, M and Natale, L and Metta, G and Rosasco, L},
doi = {10.1109/IROS.2013.6696893},
pages = {3759--3764},
title = {On the impact of learning hierarchical representations for visual recognition in robotics},
url = {},
year = {2013}

RIS format (EndNote, RefMan)

AB - Recent developments in learning sophisticated, hierarchical image representations have led to remarkable progress in the context of visual recognition. While these methods are becoming standard in modern computer vision systems, they are rarely adopted in robotics. The question arises of whether solutions, which have been primarily developed for image retrieval, can perform well in more dynamic and unstructured scenarios. In this paper we tackle this question performing an extensive evaluation of state of the art methods for visual recognition on a iCub robot. We consider the problem of classifying 15 different objects shown by a human demonstrator in a challenging Human-Robot Interaction scenario. The classification performance of hierarchical learning approaches are shown to outperform benchmark solutions based on local descriptors and template matching. Our results show that hierarchical learning systems are computationally efficient and can be used for real-time training and recognition of objects. © 2013 IEEE.
AU - Ciliberto,C
AU - Fanello,SR
AU - Santoro,M
AU - Natale,L
AU - Metta,G
AU - Rosasco,L
DO - 10.1109/IROS.2013.6696893
EP - 3764
PY - 2013///
SN - 2153-0858
SP - 3759
TI - On the impact of learning hierarchical representations for visual recognition in robotics
UR -
ER -