Speeding up pattern localization and other tasks

TLDR: Experimental results show that the early non-maxima suppression approach significantly reduces amount of computation in the case of object localization while the error rates are limited to low predefined values.

Abstract: Detection of objects in images using statistical classifiers is a well studied and documented technique. Different applications of such detectors often require selection of the image position with the highest response of the detector—they perform non-maxima suppression. This article introduces the concept of early non-maxima sup- pression, which aims to reduce necessary computations by making the non-maxima suppression decision early based on incomplete information provided by a partially evalu- ated classifier. We show that the error of one such specu- lative decision with respect to a decision made based on response of the complete classifier can be estimated by collecting statistics on unlabeled data. The article then considers a sequential strategy of multiple early non- maxima suppression tests which follows the structure of soft-cascade detectors commonly used for object detection. We also show that an optimal (fastest for requested error rate) suppression strategy can be created by a novel variant of Wald's sequential probability ratio test (SPRT) which we call the conditioned SPRT (CSPRT). Experimental results show that the early non-maxima suppression sig- nificantly reduces amount of computation in the case of object localization while the error rates are limited to low predefined values. The proposed approach notably outper- forms the state-of-the-art detectors based on WaldBoost. The potential applications of the early non-maxima suppression approach are not limited to object localization and could be applied wherever the goal is to find the strongest response of a classifier among a set of classified samples.