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.

Move, for string quartet, piano, percussion and electronics

MOVE is a composition for string quartet, piano, percussion and electronics of approximately 15-16 minutes duration in three movements. The work incorporates electronic samples either synthesized electronically by the composer or recorded from acoustic instruments. The work aims to use electronic sounds as an expansion of the tonal palette of the chamber group (rather like an extended percussion setup) as opposed to a dominating sonic feature of the music. This is done by limiting the use of electronics to specific sections of the work, and by prioritizing blend and sonic coherence in the synthesized samples. The work uses fixed electronics in such a way that allows for tempo variations in the music. Generally, a difficulty arises in that fixed “tape” parts don’t allow tempo variations; while truly “live” software algorithms sacrifice rhythmic accuracy. Sample pads, such as the Roland SPD-SX, provide an elegant solution. The latency of such a device is close enough to zero that individual samples can be triggered in real time at a range of tempi. The percussion setup in this work (vibraphone and sample pad) allows one player to cover both parts, eliminating the need for an external musician to trigger the electronics. Compositionally, momentum is used as a constructing principle. The first movement makes prominent use of ostinato and shifting meter. The second is a set of variations on a repeated harmonic pattern, with a polymetric middle section. The third is a type of passacaglia, wherein the bassline is not introduced right away, but becomes more significant later in the movement. Given the importance of visual presentation in the Internet age, the final goal of the project was to shoot HD video of a studio performance of the work for publication online. The composer recorded audio and video in two separate sessions and edited the production using Logic X and Adobe Premiere Pro. The final video presentation can be seen at geoffsheil.com/move.

Custom Analytics with Google Tag Manager: Assessing Usage Statistics on the MD-SOAR Platform

As usage metrics continue to attain an increasingly central role in library system assessment and analysis, librarians tasked with system selection, implementation, and support are driven to identify metric approaches that simultaneously require less technical complexity and greater levels of data granularity. Such approaches allow systems librarians to present evidence-based claims of platform usage behaviors while reducing the resources necessary to collect such information, thereby representing a novel approach to real-time user analysis as well as dual benefit in active and preventative cost reduction. As part of the DSpace implementation for the MD SOAR initiative, the Consortial Library Application Support (CLAS) division has begun test implementation of the Google Tag Manager analytic system in an attempt to collect custom analytical dimensions to track author- and university-specific download behaviors. Building on the work of Conrad , CLAS seeks to demonstrate that the GTM approach to custom analytics provides both granular metadata-based usage statistics in an approach that will prove extensible for additional statistical gathering in the future. This poster will discuss the methodology used to develop these custom tag approaches, the benefits of using the GTM model, and the risks and benefits associated with further implementation.

Essays on Matching Markets and Their Equilibria

Matching theory and matching markets are a core component of modern economic theory and market design. This dissertation presents three original contributions to this area. The first essay constructs a matching mechanism in an incomplete information matching market in which the positive assortative match is the unique efficient and unique stable match. The mechanism asks each agent in the matching market to reveal her privately known type. Through its novel payment rule, truthful revelation forms an ex post Nash equilibrium in this setting. This mechanism works in one-, two- and many-sided matching markets, thus offering the first mechanism to unify these matching markets under a single mechanism design framework. The second essay confronts a problem of matching in an environment in which no efficient and incentive compatible matching mechanism exists due to matching externalities. I develop a two-stage matching game in which a contracting stage facilitates subsequent conditionally efficient and incentive compatible Vickrey auction stage. Infinite repetition of this two-stage matching game enforces the contract in every period. This mechanism produces inequitably distributed social improvement: parties to the contract receive all of the gains and then some. The final essay demonstrates the existence of prices which stably and efficiently partition a single set of agents into firms and workers, and match those two sets to each other. This pricing system extends Kelso and Crawford's general equilibrium results in a labor market matching model and links one- and two-sided matching markets as well.