Parallelizing multiclass Support Vector Machines for scalable image annotation

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 MRMSVM, a distributed multiclass SVM algorithm for large scale image annotation which partitions the training dataset into smaller binary chunks and train SVM in parallel using a cluster of computers. MRMSVM is evaluated in an experimental environment showing that the distributed Multiclass SVM algorithm reduces the training time significantly while maintaining a high level of accuracy in classifications.