A distributed SVM 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. However, SVM training is notably a computationally intensive process e...

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Bibliographic Details
Main Authors: Alham, N. K., Maozhen Li, Yang Liu, Hammoud, S., Ponraj, M.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Classification algorithms
Clustering algorithms
distributed SVM
image annotation
machine learning
MapReduce
Partitioning algorithms
Support vector machine classification
Training
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Summary: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. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents MRSVM, a distributed SVM algorithm for large scale image annotation which partitions the training data set into smaller subsets and train SVM in parallel using a cluster of computing nodes. MRSVM is evaluated in an experimental environment showing that the distributed SVM algorithm reduces the training time significantly while maintaining a high level of accuracy in classifications.
DOI:10.1109/FSKD.2011.6020072