Distributed Image Processing using Different Techniques in Hadoop
AbstractWith the rapid growth of social media, the quantity of pictures being uploaded to the net is exploding. Huge quantities of pictures are shared through multi-platform services such as Snapchat, Instagram, Facebook and WhatsApp; recent studies estimate that over 1.8 billion photos are uploaded each day. However, for the most part, applications that make use of this large information have yet to emerge. Most current image process applications, designed for small-scale, native computation, don't scale well to web-sized issues with their massive necessities for machine resources and storage. The emergence of process frameworks like the Hadoop and MapReduce platform addresses the matter of providing a system for computationally intensive processing and distributed storage. However, to find out the technical complexities of developing helpful applications using Hadoop needs an outsized investment of your time and skill on the a part of the developer. As such, the pool of researchers and programmers with the numerous skills to develop applications that can use massive sets of pictures has been restricted. To handle this we've developed the Hadoop Image process Framework that provides a Hadoop-based library to support large-scale image process. The most aim of the framework is to permit developers of image process applications to leverage the Hadoop MapReduce framework while not having to master its technical details and introduce a further source of quality and error into their programs.
How to Cite
BHADRE, Parvati; S, Vishnupriya G; S, rajasree R. Distributed Image Processing using Different Techniques in Hadoop. International Journal Of Emerging Technology and Computer Science, [S.l.], v. 3, n. 3, p. 22-28, june 2018. ISSN 2455-9954. Available at: <https://aspirepublishers.com/index.php/ijetcs/article/view/214>. Date accessed: 05 july 2020.