Incremental Multiple Kernel Learning for object recognition

A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining task specific datasets of visual categories is, however, far more tedious than obtaining a generic dataset of the same classes. We propose an Incremental Multiple Kernel Learning (IMKL) approach to object recognition that initializes on a generic training database and then tunes itself to the classification task at hand. Our system simultaneously updates the training dataset as well as the weights used to combine multiple information sources. We demonstrate our system on a vehicle classification problem in a video stream overlooking a traffic intersection. Our system updates itself with images of vehicles in poses more commonly observed in the scene, as well as with image patches of the background, leading to an increase in performance. A considerable change in the kernel combination weights is observed as the system gathers scene specific training data over time. The system is also seen to adapt itself to the illumination change in the scene as day transitions to night.