Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Multiple sulfur isotope fractionations in inorganic aqueous systems

New constraints on isotope fractionation factors in inorganic aqueous sulfur systems based on theoretical and experimental techniques relevant to studies of the sulfur cycle in modern environments and the geologic rock record are presented in this dissertation. These include theoretical estimations of equilibrium isotope fractionation factors utilizing quantum mechanical software and a water cluster model approach for aqueous sulfur compounds that span the entire range of oxidation state for sulfur. These theoretical calculations generally reproduce the available experimental determinations from the literature and provide new constraints where no others are available. These theoretical calculations illustrate in detail the relationship between sulfur bonding environment and the mass dependence associated with equilibrium isotope exchange reactions involving all four isotopes of sulfur. I additionally highlight the effect of isomers of protonated compounds (compounds with the same chemical formula but different structure, where protons are bound to either sulfur or oxygen atoms) on isotope partitioning in the sulfite (S4+) and sulfoxylate (S2+) systems, both of which are key intermediates in oxidation-reduction processes in the sulfur cycle. I demonstrate that isomers containing the highest degree of coordination around sulfur (where protonation occurs on the sulfur atom) have a strong influence on isotopic fractionation factors, and argue that isomerization phenomenon should be considered in models of the sulfur cycle. Additionally, experimental results of the reaction rates and isotope fractionations associated with the chemical oxidation of aqueous sulfide are presented. Sulfide oxidation is a major process in the global sulfur cycle due largely to the sulfide-producing activity of anaerobic microorganisms in organic-rich marine sediments. These experiments reveal relationships between isotope fractionations and reaction rate as a function of both temperature and trace metal (ferrous iron) catalysis that I interpret in the context of the complex mechanism of sulfide oxidation. I also demonstrate that sulfide oxidation is a process associated with a mass dependence that can be described as not conforming to the mass dependence typically associated with equilibrium isotope exchange. This observation has implications for the inclusion of oxidative processes in environmental- and global-scale models of the sulfur cycle based on the mass balance of all four isotopes of sulfur. The contents of this dissertation provide key reference information on isotopic fractionation factors in aqueous sulfur systems that will have far-reaching applicability to studies of the sulfur cycle in a wide variety of natural settings.