Integrated Learning of Saliency, Complex Features, and Object Detectors from Cluttered Scenes

Abstract

A novel procedure for object detection from cluttered scenes is proposed. It consists of an integrated solution to the problems of learning 1) a saliency detection module tuned to a class of objects of interest, 2) a set of complex features that achieves the optimal trade-off, in a minimum probability of error sense, between discrimination and generalization ability, and 3) a large-margin object detector. All stages of the new procedure have some degree of biological motivation and this is shown to enable a computationally efficient solution that is scalable to problems containing large numbers of object classes. Experimental evidence is given in support of the arguments that different levels of feature complexity are optimal for different object classes, and that optimal features range from parts to templates, depending on the variability of the object class.