TY - JOUR
T1 - Weakly supervised scale-invariant learning of models for visual recognition
AU - Fergus, R.
AU - Perona, P.
AU - Zisserman, A.
N1 - Funding Information:
We are indebted to Li Fei-Fei, David Lowe and Andrew Blake for their insights and suggestions. We also thank Timor Kadir for advice on the feature detector. D. Roth for providing the Cars (Side) dataset. Funding was provided by National Science Foundation Engineering Research Center for Neuromorphic Systems Engineering, the UK EPSRC, EC Project CogViSys and PASCAL Network of Excellence.
PY - 2007/3
Y1 - 2007/3
N2 - We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. Category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly described in the model. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the model"s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The flexible nature of the model is demonstrated by results over six diverse object categories including geometrically constrained categories (e.g. faces, cars) and flexible objects (such as animals).
AB - We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. Category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly described in the model. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the model"s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The flexible nature of the model is demonstrated by results over six diverse object categories including geometrically constrained categories (e.g. faces, cars) and flexible objects (such as animals).
KW - Constellation model
KW - Object recognition
KW - Parts and structure model
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=33750397657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750397657&partnerID=8YFLogxK
U2 - 10.1007/s11263-006-8707-x
DO - 10.1007/s11263-006-8707-x
M3 - Article
AN - SCOPUS:33750397657
VL - 71
SP - 273
EP - 303
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
IS - 3
ER -