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What is Hadoop Image Processing Interface (HIPI)

Chapter 18
Handbook of Research on Big Data Storage and Visualization Techniques
HIPI is an image processing library designed to be used with the Apache Hadoop MapReduce parallel programming framework. HIPI facilitates efficient and high throughput image processing with MapReduce style parallel programs typically executed on a cluster (University of Virginia Computer Graphics Lab, 2016). It provides a solution for how to store a large collection of images on the Hadoop Distributed File System (HDFS) and make them available for efficient distributed processing (University of Virginia Computer Graphics Lab, 2016). HIPI also provides integration with OpenCV, a popular open source library that contains many computer vision algorithms. The latest release of HIPI has been tested with Hadoop 2.7.1 (University of Virginia Computer Graphics Lab, 2016).
Published in Chapter:
The Image as Big Data Toolkit: An Application Case Study in Image Analysis, Feature Recognition, and Data Visualization
Kerry E. Koitzsch (Kildane Software Technologies Inc., USA)
Copyright: © 2018 |Pages: 52
DOI: 10.4018/978-1-5225-3142-5.ch018
Abstract
This chapter is a brief introduction to the Image As Big Data Toolkit (IABDT), a Java-based open source framework for performing a variety of distributed image processing and analysis tasks. IABDT has been developed over the last two years in response to the rapid evolution of Big Data architectures and technologies, distributed and image processing systems. This chapter presents an architecture for image analytics that uses Big Data storage and compression methods. A sample implementation of our image analytic architecture called the Image as Big Data Toolkit (IABDT) addresses some of the most frequent challenges experienced by the image analytics developer. Baseline applications developed with IABDT, status of the toolkit and directions for future extension with emphasis on image display, presentation, and reporting case studies are discussed to motivate our design and technology stack choices. Sample applications built using IABDT, as well as future development plans for IABDT are discussed.
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