A resource aware MapReduce based parallel SVM for large scale image classification

Journal article

Guo, W., Khalid, N., Liu, Y., Li, M., Qi, M., Guo, W., Khalid, N., Liu, Y., Li, M. and Qi, M. 2015. A resource aware MapReduce based parallel SVM for large scale image classification. Neural Processing Letters. 44 (1), pp. 161-184.
AuthorsGuo, W., Khalid, N., Liu, Y., Li, M., Qi, M., Guo, W., Khalid, N., Liu, Y., Li, M. and Qi, M.
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. However, SVM training is notably a computationally intensive process especially when the training dataset is large.

This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets 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 RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments.

The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classifications

KeywordsParallel SVM; MapReduce; image classification and annotation; load balancing
Year2015
JournalNeural Processing Letters
Journal citation44 (1), pp. 161-184
PublisherKluwer
ISSN1370-4621
Official URLhttps://dl.acm.org/citation.cfm?id=J580&picked=prox
Publication dates
Online18 Sep 2015
Publication process dates
Deposited03 Apr 2018
Accepted author manuscript
Output statusPublished
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https://repository.canterbury.ac.uk/item/88q11/a-resource-aware-mapreduce-based-parallel-svm-for-large-scale-image-classification

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