Parallelizing multiclass support vector machines for scalable image annotation

Abstract

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them, Support Vector Machines (SVMs) are used extensively due to their generalization properties. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. Notably, SVM training is a computationally intensive process especially when the training dataset is large. This paper presents a resource aware parallel multiclass SVM algorithm (named RAMSMO) for large-scale image annotation which partitions the training dataset into smaller binary chunks and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm-based load balancing scheme is designed to optimize the performance of RAMSMO in balancing the computation of multiclass data chunks in heterogeneous computing environments. RAMSMO is evaluated in both experimental and simulation environments, and the results show that it reduces the training time significantly while maintaining a high level of accuracy in classifications.