Abstract
The amount of today's image collections has exploded to petabytes of data. A fair amount of time cannot be allotted for the analysis of such massive datasets using a personal computer. As a result, distributed computing is required for current image collection mining. This work used multi-nodes-computers for image mining in order to improve reliability, fall tolerance, and time efficiency. Because of this, the data was divided up across the nodes in the Hadoop multi-node cluster, and the results were then compiled to create an image clustering algorithm. One master node and two slave nodes were used to test our technique on a huge dataset. Using multi-node Hadoop, we found that we could get a speed-up in implementing as high as a single-node Hadoop implementation.
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