Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
Search Results
Item Efficient Image Segmentation and Segment-Based Analysis in Computer Vision Applications(2015) Soares, Joao V. B.; Jacobs, David W; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This 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.Item Recognizing Visual Categories by Commonality and Diversity(2015) Choi, Jonghyun; Davis, Larry Steven; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Visual categories refer to categories of objects or scenes in the computer vision literature. Building a well-performing classifier for visual categories is challenging as it requires a high level of generalization as the categories have large within class variability. We present several methods to build generalizable classifiers for visual categories by exploiting commonality and diversity of labeled samples and the cat- egory definitions to improve category classification accuracy. First, we describe a method to discover and add unlabeled samples from auxil- iary sources to categories of interest for building better classifiers. In the literature, given a pool of unlabeled samples, the samples to be added are usually discovered based on low level visual signatures such as edge statistics or shape or color by an unsupervised or semi-supervised learning framework. This method is inexpensive as it does not require human intervention, but generally does not provide useful information for accuracy improvement as the selected samples are visually similar to the existing set of samples. The samples added by active learning, on the other hand, provide different visual aspects to categories and contribute to learning a better classifier, but are expensive as they need human labeling. To obtain high quality samples with less annotation cost, we present a method to discover and add samples from unlabeled image pools that are visually diverse but coherent to cat- egory definition by using higher level visual aspects, captured by a set of learned attributes. The method significantly improves the classification accuracy over the baselines without human intervention. Second, we describe now to learn an ensemble of classifiers that captures both commonly shared information and diversity among the training samples. To learn such ensemble classifiers, we first discover discriminative sub-categories of the la- beled samples for diversity. We then learn an ensemble of discriminative classifiers with a constraint that minimizes the rank of the stacked matrix of classifiers. The resulting set of classifiers both share the category-wide commonality and preserve diversity of subcategories. The proposed ensemble classifier improves recognition accuracy significantly over the baselines and state-of-the-art subcategory based en- semble classifiers, especially for the challenging categories. Third, we explore the commonality and diversity of semantic relationships of category definitions to improve classification accuracy in an efficient manner. Specif- ically, our classification model identifies the most helpful relational semantic queries to discriminatively refine the model by a small amount of semantic feedback in inter- active iterations. We improve the classification accuracy on challenging categories that have very small numbers of training samples via transferred knowledge from other related categories that have a lager number of training samples by solving a semantically constrained transfer learning optimization problem. Finally, we summarize ideas presented and discuss possible future work.