SHIFTING SCALES, ADJUSTING LENSES: A FRAMEWORK FOR INVESTIGATING BALTIMORE’S URBAN VACANCY

This thesis addresses contemporary gaps of vacancy within literature by using qualitative and quantitative methods and tools to determine the quantity, location, and interspatial relationships of vacant buildings and lots located in Baltimore Maryland. Spatial analyses were conducted to answer three questions of vacancy: 1) how many vacant lots and buildings exist, 2) whether there are spatial patterns of vacancy, such as clustering around geographic locations or within watersheds, and 3) how to prioritize intervention opportunities that respond to the city's larger issues? Using the city’s vacant lot and building data-sets, two concepts emerged from these investigations. First, Utilized Landscapes as a classification system that identifies lands that serve a function but have un-traditional qualities that make them susceptible to being labeled “vacant.” Second, the development of Transitional Zones, geographical areas with a high density of vacant buildings or lots that should be prioritized.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.