A resource aware MapReduce based parallel SVM for large scale image classification
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.
|Authors||Guo, W., Khalid, N., Liu, Y., Li, M., Qi, M., Guo, W., Khalid, N., Liu, Y., Li, M. and Qi, M.|
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
|Keywords||Parallel SVM; MapReduce; image classification and annotation; load balancing|
|Journal||Neural Processing Letters|
|Journal citation||44 (1), pp. 161-184|
|Online||18 Sep 2015|
|Publication process dates|
|Deposited||03 Apr 2018|
|Accepted author manuscript|
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