Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

The Named-State Register File

This thesis introduces the Named-State Register File, a fine-grain, fully-associative register file. The NSF allows fast context switching between concurrent threads as well as efficient sequential program performance. The NSF holds more live data than conventional register files, and requires less spill and reload traffic to switch between contexts. This thesis demonstrates an implementation of the Named-State Register File and estimates the access time and chip area required for different organizations. Architectural simulations of large sequential and parallel applications show that the NSF can reduce execution time by 9% to 17% compared to alternative register files.