One of the most fundamental problems in the treatment of high-dimensional data is classification of a cloud of points in R^D into several sub-classes based on training data. An important such task is the pattern detection problem in images, which requires a separation between ‘Target’ and ‘Clutter’ classes, where every instance of a pattern in each of these classes appears as a sequence of D pixels. In most cases, the following properties hold true for the target detection task: (i) the probability of the ‘Target’ class is substantially smaller compared to that of the ‘Clutter’ ; (ii) the volume occupied by the target class in R^D is far smaller than that held by the clutter set ; and (iii) The target set is either convex or can be divided to several sub-sets each being convex.
In this talk we describe a new classifier that exploits these properties, yielding a low complexity yet effective target detection algorithm. This algorithm, called the Maximal Rejection Classifier (MRC), is based on successive rejection operations. Each such rejection stage is performed using a linear projection followed by thresholding. The projection direction is designed to maximize the number of rejected ‘Clutter’ points from further consideration. An application of detecting frontal and vertical faces in images is demonstrated using the MRC with encouraging results.
The target detection problem is defined as the need to separate targets from clutter instances. Among the many example-based techniques for the solution of this problem, the family of rejection-based classifiers are consistently exhibiting state-of-the-art accuracy while being the fastest. This rejection-based approach advocates the use of large set of weak-classifiers chained sequentially. After application of each such atom-block, a rejection of some of the clutter is performed while guaranteeing no loss of targets.
While intuitively appealing, theoretic background for this method was gathered only recently. Some roots of it can be traced to the boosting algorithm and the decision tree methods – two wide fields of research in machine learning that concentrate on using multiple weak-classifiers for the construction of a complicated overall machine. Rejection as a concept was proposed and analyzed by Nayar and Baker, with emphasis on the multi-class problems. More recently Elad, Hel-Or, and Keshet proposed the Maximal-rejection-Classifier (MRC), and employed it to the face detection problem. To conclude this list of works on the rejection-based idea, we should mention the work of Viola and Johns on the face detection problem using sub-linear weak-classifiers joined via boosting. In this talk I’ll survey the various contributions to the rejection idea and its efficient implementation on face detection problem.
Pattern detection problems require a separation between two classes, ‘Target’ and ‘Clutter’, where the probability of the former is substantially smaller than that of the latter. We describe a new classifier that exploits this property, yielding a low complexity yet effective target detection algorithm. This Maximal Rejection Classifier (MRC) algorithm is based on successive rejection operations. Each rejection stage is performed using a linear projection followed by thresholding. The projection direction is designed to maximize the number of ‘Clutter’ points rejected from further consideration. An application of detecting frontal and vertical faces in images is demonstrated using the MRC, with encouraging results.