Image Segmentation Using Dictionary Learning and Compressed Random Features

Geoffrey Bull, Junbin Gao, Michael Antolovich

Research output: Book chapter/Published conference paperConference paper

Abstract

Image segmentation seeks to partition the pixels in images into distinct regions to assist other image processing functions such as object recognition. Over the last few years dictionary learning methods have become very popular for image processing tasks such as denoising, and recently structured low rank dictionary learning has been shown to be capable of promising results for recognition tasks. This paper investigates the suitability of dictionary learning for image segmentation. A structured low rank dictionary learning algorithm is developed to segment images using compressed sensed features from image patches. To enable a supervised learning approach, classes of pixels in images are designated using training scribbles. A classifier is then learned from these training pixels and subsequently used to classify all other pixels in the images to form the segmentations. A number of dictionary learning models are compared together with K-means/nearest neighbour and support vector machine classifiers.
Original languageEnglish
Title of host publicationProceedings of the 2014 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781479954094
DOIs
Publication statusPublished - 2014
Event2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Novotel Wollongong Northbeach Hotel, Wollongong, Australia
Duration: 25 Nov 201427 Nov 2014
https://ssl.informatics.uow.edu.au/dicta2014/

Conference

Conference2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CityWollongong
Period25/11/1427/11/14
OtherThe International Conference on Digital Image Computing: Techniques and Applications (DICTA) is an International Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established as a biennial conference in 1991 and became an annual event in 2007. It is the premier conference of the Australian Pattern Recognition Society (APRS).
Internet address

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  • Cite this

    Bull, G., Gao, J., & Antolovich, M. (2014). Image Segmentation Using Dictionary Learning and Compressed Random Features. In Proceedings of the 2014 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2014.7008112