Efficient Image Segmentation and Segment-Based Analysis in Computer Vision Applications

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dc.contributor.advisorJacobs, David Wen_US
dc.contributor.authorSoares, Joao V. B.en_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2015-06-26T05:36:27Z
dc.date.available2015-06-26T05:36:27Z
dc.date.issued2015en_US
dc.description.abstractThis dissertation focuses on efficient image segmentation and segment-based object recognition in computer vision applications. Special attention is devoted to analyzing shape, of particular importance for our two applications: plant species identification from leaf photos, and object classification in remote sensing images. Additionally, both problems are bound by efficiency, constraining the choice of applicable methods: leaf recognition results are to be used within an interactive system, while remote sensing image analysis must scale well over very large image sets. Leafsnap was the first mobile app to provide automatic recognition of tree species, currently counting with over 1.7 million downloads. We present an overview of the mobile app and corresponding back end recognition system, as well as a preliminary analysis of user-submitted data. More than 1.7 million valid leaf photos have been uploaded by users, 1.3 million of which are GPS-tagged. We then focus on the problem of segmenting photos of leaves taken against plain light-colored backgrounds. These types of photos are used in practice within Leafsnap for tree species recognition. A good segmentation is essential in order to make use of the distinctive shape of leaves for recognition. We present a comparative experimental evaluation of several segmentation methods, including quantitative and qualitative results. We then introduce a custom-tailored leaf segmentation method that shows superior performance while maintaining computational efficiency. The other contribution of this work is a set of attributes for analysis of image segments. The set of attributes is designed for use in knowledge-based systems, so they are selected to be intuitive and easily describable. The attributes can also be computed efficiently, to allow applicability across different problems. We experiment with several descriptive measures from the literature and encounter certain limitations, leading us to introduce new attribute formulations and more efficient computational methods. Finally, we experiment with the attribute set on our two applications: plant species identification from leaf photos and object recognition in remote sensing images.en_US
dc.identifierhttps://doi.org/10.13016/M2Z03G
dc.identifier.urihttp://hdl.handle.net/1903/16612
dc.language.isoenen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledattributesen_US
dc.subject.pquncontrolledimage segmentationen_US
dc.subject.pquncontrolledobject recognitionen_US
dc.subject.pquncontrolledsegmentation evaluationen_US
dc.subject.pquncontrolledshape analysisen_US
dc.subject.pquncontrolledspecies identificationen_US
dc.titleEfficient Image Segmentation and Segment-Based Analysis in Computer Vision Applicationsen_US
dc.typeDissertationen_US

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