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Visual Scene Understanding through Semantic Segmentation

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dc.contributor.advisor Košecká, Jana Singh, Gautam
dc.creator Singh, Gautam en_US 2015-02-12T02:59:55Z 2015-02-12T02:59:55Z 2014 en_US
dc.description.abstract The problem of visual scene understanding entails recognizing the semantic constituents of a scene and the complex interactions that occur between them. Development of algorithms for semantic segmentation, which requires the simultaneous segmentation of an image into regions and the classification of these regions into semantic categories, is at the heart of this problem. This dissertation presents methods that provide improvements to the state of the art in semantic segmentation of images and investigates the use of the obtained semantic segmentation output for related image retrieval and classification tasks. We present a method for non-parametric semantic segmentation of images which can effectively work on image datasets with a large number of categories. The method exploits query time feature channel relevance and also introduces the semantic label descriptor for improving the semantic segmentation output by retrieving images which share semantically similar spatial layouts. We further demonstrate how to associate accurate confidences with the resulting semantic segmentation through the use of the strangeness measure. We show how this measure can be applied for confidence ranking of unlabeled images and associate high uncertainty scores with images containing unfamiliar semantic categories. We then demonstrate the use of semantic segmentation output for additional tasks such as scene categorization, learning related semantic concepts and content based image retrieval.
dc.format.extent 112 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Gautam Singh en_US
dc.subject Computer science en_US
dc.subject Computer Vision en_US
dc.subject Machine Learning en_US
dc.subject Scene Understanding en_US
dc.subject Semantic Segmentation en_US
dc.title Visual Scene Understanding through Semantic Segmentation en_US
dc.type Dissertation en Doctoral en Computer Science en George Mason University en

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