Real-Time Big Data Image Classification under MapReduce Framework

As an important part of big data,image data contains abundant knowledge.The classification of image data has been widely used,while nowadays,the traditional classification methods are unable to meet the need of real-time computing.To solve this problem,we propose a parallel online extreme learning machine algorithm.Firstly,with the theory of online extreme learning machine,we calculate the output weight matrix of hidden layer nodes.Secondly,this matrix is partitioned to several matrix blocks based on the characteristics of the MapReduce framework so as to substitute the original large-scale matrix multiplication operation,and the matrix blocks are calculated in different work nodes in parallel.Finally,the values in calculation nodes are merged by the key values and we get the classifier.Under the premise that the original calculation accuracy is guaranteed,we extend the online extreme learning machine algorithm to the MapReduce framework and the classification experiment results on massive image data,taking facial image data as an example,show that the algorithm in this paper can classify massive image data fast and accurately.