Sharing Visual Features for Multiclass and Multiview Object Detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. We present a multi-task learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the run-time cost of the classifier, is observed to scale app...

• Type: Journal
• Year: 2007
• Where: PAMI
• Authors: Antonio Torralba, Kevin P. Murphy, William T. Freeman
• Keywords: Large Number, Multi-task Learning Procedure, PAMI 2007, Sample Complexity