Efficient 2D image segmentation
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Graham-Knight, John Brandon
issued: 2020-08-14T21:04:04Z
issued: 2020
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Image segmentation is a popular topic enabled by rapid advances in neural network processing. There is also a trend towards computational efficiency in segmentation algorithms, brought about by economic and environmental interests, and often acutely motivated by constraints in how the solutions are deployed. Recent exploration of neural network computational efficiency has largely focused on the depth and width of the network, as well as image size. Ensembling and gradient boosting are two well-known methods for increasing performance through a collection of smaller networks. Ensembling is used when different models produce largely uncorrelated predictions; the combination of these predictions reduces overall error. Gradient boosting produces correlated models such that the sum of their losses is minimized; successive models are trained using the residual loss of previous models, with each new learner correcting mistakes of past learners. This thesis seeks to explore the scalability and efficiency of image segmentation neural networks by including the techniques of ensembling and gradient boosting. The approach is evaluated on both the Severstal Steel Defect Detection and Kidney Tumor Segmentation datasets, though it could easily be applied to any two-dimensional image segmentation task. The final boosted model produces results comparable to the baseline in 5 of the 6 evaluated classes, while using only 2.5% of the trainable parameters. The success of the approach has significant implications for deployed applications; not only is the overall network much smaller, the miniature sub-networks can each be calculated independently and aggregated with simple addition and averaging. This introduces a real possibility for efficient distributed inferencing, decoupling performance from the maximum size calculable by a single piece of hardware.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75547
note: Efficient 2D Image SegmentationbyJohn Brandon Graham-KnightB.Eng., The University of Victoria, 2010A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)August 2020John Brandon Graham-Knight, 2020This work is licensed under a Creative CommonsAttribution-NonCommercial-ShareAlike 4.0 International License.The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Efficient 2D Image Segmentationsubmitted by John Brandon Graham-Knight in partial fulfilment of therequirements of the degree of Master of ScienceDr. Patricia Lasserre, Irving K. Barber Faculty of ScienceCo-SupervisorDr. Homayoun Najjaran, School of EngineeringCo-SupervisorDr. Mohammad Khalad Hasan, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Apurva Narayan, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Kenneth Chau, School of EngineeringUniversity ExamineriiAbstractImage segmentation is a popular topic enabled by rapid advances inneural network processing. There is also a trend towards computationalefficiency in segmentation algorithms, brought about by economic and envi-ronmental interests, and often acutely motivated by constraints in how thesolutions are deployed. Recent exploration of neural network computationalefficiency has largely focused on the depth and width of the network, aswell as image size. Ensembling and gradient boosting are two well-knownmethods for increasing performance through a collection of smaller networks.Ensembling is used when different models produce largely uncorrelated pre-dictions; the combination of these predictions reduces overall error. Gradi-ent boosting produces correlated models such that the sum of their lossesis minimized; successive models are trained using the residual loss of pre-vious models, with…
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0392788
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-ShareAlike 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-sa/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75547