Array based image processing using Bayesian techniques typically involves constructing and computing with a Bayes network, a graph in which the nodes are considered as random variables and the graph edges are conditional dependencies. Image feature extraction is performed in the usual way and Bayesian inferential processes are applied to the extracted feature data. Random variables and conditional dependencies are standard Bayesian concepts from the fundamental Bayesian statistics. Following Opper and Winther (Smolla, Bartlett, Scholkopf & Schuurmans, 2001 AU123: The in-text citation "Smolla, Bartlett, Scholkopf & Schuurmans, 2001" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ), Bayesian optimal prediction can be characterized as: (19) , (Smolla, Bartlett, Scholkopf & Schuurmans, 2001 AU124: The in-text citation "Smolla, Bartlett, Scholkopf & Schuurmans, 2001" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ) where we are in essence performing an inference task of the kind described in the paper. Our goal is prediction of correct labels for points x, the ‘y’ quantity, is the binary optimal prediction, (Smolla, Bartlett, Scholkopf & Schuurmans, 2001 AU125: The in-text citation "Smolla, Bartlett, Scholkopf & Schuurmans, 2001" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ) D m is the training set, the data set used to train the classifiers: (Smolla, Bartlett, Scholkopf & Schuurmans,, 2001) the Bayesian ‘prior’ p is the probability distribution that would express belief about this quantity before existing evidence is accounted for. Object hypotheses, prediction and sensor fusion are typical problem areas for Bayesian image processing, and many serial and distributed versions of standard Bayesian algorithms such as naïve Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes, Averaged One-Dependence Estimators (AODE), Bayesian Belief Network (BBN) and Bayesian Network have been implemented for mainstream toolkits like MLlib, Mahout and H2O.
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.