Adapting Classifiers To Changing Class Priors During Deployment

Conventional classifiers are trained and evaluated using balanced data sets in which all classes are equally present. Classifiers are now trained on large data sets such as ImageNet, and are now able to classify hundreds (if not thousands) of different classes. On one hand, it is desirable to train such general-purpose classifier on a very large number of classes so that it performs well regardless of the settings in which it is deployed. On the other hand, it is unlikely that all classes known to the classifier will occur in every deployment scenario, or that they will occur with the same prior probability. In reality, only a relatively small subset of the known classes may be present in a particular setting or environment. For example, a classifier will encounter mostly animals if its deployed in a zoo or for monitoring wildlife, aircraft and service vehicles at an airport, or various types of automobiles and commercial vehicles if it is used for monitoring traffic. Furthermore, the exact class priors are generally unknown and can vary over time. In this paper, we explore different methods for estimating the class priors based on the output of the classifier itself. We then show that incorporating the estimated class priors in the overall decision scheme enables the classifier to increase its run-time accuracy in the context of its deployment scenario

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system

Theoretical Framework For The Design Of Purely Real Synthetic-Discriminant-Function-Type Correlation Filters

A general algorithm for synthesizing purely real correlation filters in the frequency domain is developed by using the method of Lagrange multipliers. This method can be applied to filters that are derived by using linearly constrained quadratic minimization. The synthesis of purely real versions of minimum average correlation energy filters, minimum-variance synthetic discriminant functions, and other synthetic-discriminant-function-type filters is discussed to illustrate this approach. Their performance is found to be somewhat less than that of the original complex filters but still adequate for practical applications. The main advantage of this approach is that optimum purely real filters can be generated that are easy to implement in spatial fight modulators without holograms and that yield the correlation output on the zero-order beam. © 1992 Optical Society of America

Methods for automatic target recognition by use of electro-optic sensors: introduction to the feature issue

Introduction to the feature issu

Automatic Target Detection And Recognition Approaches For Unattended Electro-Optical Sensors

Automatic target detection and recognition (ATD/R) remains a challenging problem for unmanned and unattended systems. Promising solutions using Electro-Optical sensors such as LADARs, FLIRs, and TVs are evolving. The key issues are not only the performance of the individual sensors, but also the mutual calibration of the sensors and their collective behavior. This paper presents an overview of the challenges encountered in two separate ATD/R scenarios, and the methods that have been proposed for addressing them. Specifically, advanced techniques that exploit multiple views, collaborative sensor behavior and new sensing paradigms are reviewed and the concepts are illustrated by means of several examples