Towards Fast Multimedia Feature Extraction: Hadoop or Storm

Abstract

The current explosion of data accelerated evolution of various content-based indexing techniques that allow to efficiently search in multimedia data such as images. However, index able features must be first extracted from the raw images before the indexing. This necessary step can be very time consuming for large datasets thus parallelization is desirable to speed the process up. In this paper, we experimentally compare two approaches to distribute the task among multiple machines: the Apache Hadoop and the Apache Storm projects.