Generative topic modeling in image data mining and bioinformatics studies

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model is proposed for co-existing image features and annotations, in which new latent variables are introduced to allow for more flexible sampling of word topics and visual topics. A perspective hierarchical Dirichlet process (pHDP) model is proposed to deal with user-tagged image modeling, associating image features with image tags and incorporating the user’s perspectives into the image tag generation process. It’s also shown that in mining large scale text corpora of natural language descriptions, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. Novel generative topic models are also introduced to meta-genomics studies. The experimental results show that the generative topic model can be used to model the taxon abundance information obtained by the homology-based approach and study the microbial core. It also shows that latent topic modeling can be used to characterize core and distributed genes within a species and to correlate similarities between genes and their functions. A further study on the functional elements derived from the non-redundant CDs catalogue shows that the configuration of functional groups encoded in the gene-expression data of meta-genome samples can be inferred by applying probabilistic topic modeling to functional elements. Furthermore, an extended HDP model is introduced to infer functional basis from detected enterotypes. The latent topics estimated from human gut microbial samples are evidenced by the recent discoveries in fecal microbiota study, which demonstrate the effectiveness of the proposed models.Ph.D., Information Systems -- Drexel University, 201

Semantic Attributes for Transfer Learning in Visual Recognition

Angetrieben durch den Erfolg von Deep Learning Verfahren wurden in Bezug auf künstliche Intelligenz erhebliche Fortschritte im Bereich des Maschinenverstehens gemacht. Allerdings sind Tausende von manuell annotierten Trainingsdaten zwingend notwendig, um die Generalisierungsfähigkeit solcher Modelle sicherzustellen. Darüber hinaus muss das Modell jedes Mal komplett neu trainiert werden, sobald es auf eine neue Problemklasse angewandt werden muss. Dies führt wiederum dazu, dass der sehr kostenintensive Prozess des Sammelns und Annotierens von Trainingsdaten wiederholt werden muss, wodurch die Skalierbarkeit solcher Modelle erheblich begrenzt wird. Auf der anderen Seite bearbeiten wir Menschen neue Aufgaben nicht isoliert, sondern haben die bemerkenswerte Fähigkeit, auf bereits erworbenes Wissen bei der Lösung neuer Probleme zurückzugreifen. Diese Fähigkeit wird als Transfer-Learning bezeichnet. Sie ermöglicht es uns, schneller, besser und anhand nur sehr weniger Beispiele Neues zu lernen. Daher besteht ein großes Interesse, diese Fähigkeit durch Algorithmen nachzuahmen, insbesondere in Bereichen, in denen Trainingsdaten sehr knapp oder sogar nicht verfügbar sind. In dieser Arbeit untersuchen wir Transfer-Learning im Kontext von Computer Vision. Insbesondere untersuchen wir, wie visuelle Erkennung (z.B. Objekt- oder Aktionsklassifizierung) durchgeführt werden kann, wenn nur wenige oder keine Trainingsbeispiele existieren. Eine vielversprechende Lösung in dieser Richtung ist das Framework der semantischen Attribute. Dabei werden visuelle Kategorien in Form von Attributen wie Farbe, Muster und Form beschrieben. Diese Attribute können aus einer disjunkten Menge von Trainingsbeispielen gelernt werden. Da die Attribute eine doppelte, d.h. sowohl visuelle als auch semantische, Interpretation haben, kann Sprache effektiv genutzt werden, um den Übertragungsprozess zu steuern. Dies bedeutet, dass Modelle für eine neue visuelle Kategorie nur anhand der sprachlichen Beschreibung erstellt werden können, indem relevante Attribute selektiert und auf die neue Kategorie übertragen werden. Die Notwendigkeit von Trainingsbildern entfällt durch diesen Prozess jedoch vollständig. In dieser Arbeit stellen wir neue Lösungen vor, semantische Attribute zu modellieren, zu übertragen, automatisch mit visuellen Kategorien zu assoziieren, und aus sprachlichen Beschreibungen zu erkennen. Zu diesem Zweck beleuchten wir die attributbasierte Erkennung aus den folgenden vier Blickpunkten: 1) Anders als das gängige Modell, bei dem Attribute global gelernt werden müssen, stellen wir einen hierarchischen Ansatz vor, der es ermöglicht, die Attribute auf verschiedenen Abstraktionsebenen zu lernen. Wir zeigen zudem, wie die Struktur zwischen den Kategorien effektiv genutzt werden kann, um den Lern- und Transferprozess zu steuern und damit diskriminative Modelle für neue Kategorien zu erstellen. Mit einer gründlichen experimentellen Analyse demonstrieren wir eine deutliche Verbesserung unseres Modells gegenüber dem globalen Ansatz, insbesondere bei der Erkennung detailgenauer Kategorien. 2) In vorherrschend attributbasierten Transferansätzen überwacht der Benutzer die Zuordnung zwischen den Attributen und den Kategorien. Wir schlagen in dieser Arbeit vor, die Verbindung zwischen den beiden automatisch und ohne Benutzereingriff herzustellen. Unser Modell erfasst die semantischen Beziehungen, welche die Attribute mit Objekten koppeln, um ihre Assoziationen vorherzusagen und unüberwacht auszuwählen welche Attribute übertragen werden sollen. 3) Wir umgehen die Notwendigkeit eines vordefinierten Vokabulars von Attributen. Statt dessen schlagen wir vor, Enyzklopädie-Artikel zu verwenden, die Objektkategorien in einem freien Text beschreiben, um automatisch eine Menge von diskriminanten, salienten und vielfältigen Attributen zu entdecken. Diese Beseitigung des Bedarfs eines benutzerdefinierten Vokabulars ermöglicht es uns, das Potenzial attributbasierter Modelle im Kontext sehr großer Datenmengen vollends auszuschöpfen. 4) Wir präsentieren eine neuartige Anwendung semantischer Attribute in der realen Welt. Wir schlagen das erste Verfahren vor, welches automatisch Modestile lernt, und vorhersagt, wie sich ihre Beliebtheit in naher Zukunft entwickeln wird. Wir zeigen, dass semantische Attribute interpretierbare Modestile liefern und zu einer besseren Vorhersage der Beliebtheit von visuellen Stilen im Vergleich zu anderen Darstellungen führen

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic. As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes. The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions. To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making

Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user\u27s high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic clustering and feature weighting algorithm based on robust modeling of the Generalized Dirichlet (GD) finite mixture. Robust estimation of the mixture model parameters is achieved by incorporating two complementary types of membership degrees. The first one is a posterior probability that indicates the degree to which a point fits the estimated distribution. The second membership represents the degree of typicality and is used to indentify and discard noise points. Robustness to noisy and irrelevant features is achieved by transforming the data to make the features independent and follow Beta distribution, and learning optimal relevance weight for each feature subset within each cluster. We extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. We also outline a semi-supervised version of the proposed algorithm. In the second component of our system consists of a novel approach to unsupervised image annotation. Our approach is based on: (i) the proposed semi-supervised possibilistic clustering; (ii) a greedy selection and joining algorithm (GSJ); (iii) Bayes rule; and (iv) a probabilistic model that is based on possibilistic memebership degrees to annotate an image. The third component of the proposed system consists of an image retrieval framework based on multi-modal similarity propagation. The proposed framework is designed to deal with two data modalities: low-level visual features and high-level textual keywords generated by our proposed image annotation algorithm. The multi-modal similarity propagation system exploits the mutual reinforcement of relational data and results in a nonlinear combination of the different modalities. Specifically, it is used to learn the semantic similarities between images by leveraging the relationships between features from the different modalities. The proposed image annotation and retrieval approaches are implemented and tested with a standard benchmark dataset. We show the effectiveness of our clustering algorithm to handle high dimensional and noisy data. We compare our proposed image annotation approach to three state-of-the-art methods and demonstrate the effectiveness of the proposed image retrieval system