Understanding Objects in the Visual World
| dc.contributor.advisor | Davis, Larry S | en_US |
| dc.contributor.author | Ahmed, Ejaz | en_US |
| dc.contributor.department | Computer Science | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2015-06-26T05:47:02Z | |
| dc.date.available | 2015-06-26T05:47:02Z | |
| dc.date.issued | 2015 | en_US |
| dc.description.abstract | One way to understand the visual world is by reasoning about the objects present in it: their type, their location, their similarities, their layout etc. Despite several successes, detailed recognition remains a challenging tasks for current computer vision systems. This dissertation focuses on building systems that improve on the state-of-the-art on several fronts. On one hand, we propose better representations of visual categories that enable more accurate reasoning about their properties. To learn such representations, we employ machine learning methods that leverage the power of big-data. On the other hand, we present solutions to make current frameworks more efficient without losing on performance. The first part of the dissertation focuses on improvements in efficiency. We first introduce a fast automated mechanism for selecting a diverse set of discriminative filters and show that one can efficiently learn a universal model of filter "goodness" based on properties of the filter itself. As an alternative to the expensive evaluation of filters, which is often the bottleneck in many techniques, our method has the potential of dramatically altering the trade-off between the accuracy of a filter based method and the cost of training. Second, we present a method for linear dimensionality reduction which we call composite discriminant factor analysis (CDF). CDF searches for a discriminative but compact feature subspace in which the classifiers can be trained, leading to an order of magnitude saving in detection time. In the second part, we focus on the problem of person re-identification, an important component of surveillance systems. We present a deep learning architecture that simultaneously learns features and computes their corresponding similarity metric. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. We propose new layers which capture local relationships among mid-level features, produce a high-level summary of these relationships and spatially integrate them to give a holistic representation. In the final part, we present a semantic object selection framework that uses natural language input to perform image editing. In the general context of interactive object segmentation, many of the methods that utilize user input (such as mouse clicks and mouse strokes) often require significant user intervention. In this work, we present a system with a far simpler input method: the user only needs to give the name of the desired object. For this problem we present a solution which borrows ideas from image retrieval, segmentation propagation, object localization and convolution neural networks. | en_US |
| dc.identifier | https://doi.org/10.13016/M2N911 | |
| dc.identifier.uri | http://hdl.handle.net/1903/16684 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Computer science | en_US |
| dc.subject.pquncontrolled | Computer Vision | en_US |
| dc.subject.pquncontrolled | Deep Learning | en_US |
| dc.subject.pquncontrolled | Filter Selection | en_US |
| dc.subject.pquncontrolled | Object Detection | en_US |
| dc.subject.pquncontrolled | Object Selection | en_US |
| dc.subject.pquncontrolled | Person Re-Identification | en_US |
| dc.title | Understanding Objects in the Visual World | en_US |
| dc.type | Dissertation | en_US |
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