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AdaBoost is an important algorithm in machine learning and is being widelyused in object detection. AdaBoost works by iteratively selecting the bestamongst weak classifiers, and then combines several weak classifiers to obtaina strong classifier. Even though AdaBoost has proven to be very effective, itslearning execution time can be quite large depending upon the application e.g.,in face detection, the learning time can be several days. Due to its increasinguse in computer vision applications, the learning time needs to be drasticallyreduced so that an adaptive near real time object detection system can beincorporated. In this paper, we develop a hybrid parallel and distributedAdaBoost algorithm that exploits the multiple cores in a CPU via light weightthreads, and also uses multiple machines via a web service softwarearchitecture to achieve high scalability. We present a novel hierarchical webservices based distributed architecture and achieve nearly linear speedup up tothe number of processors available to us. In comparison with the previouslypublished work, which used a single level master-slave parallel and distributedimplementation [1] and only achieved a speedup of 2.66 on four nodes, weachieve a speedup of 95.1 on 31 workstations each having a quad-core processor,resulting in a learning time of only 4.8 seconds per feature.

Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines