Proposal Flow: Semantic Correspondences from Object Proposals

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that the corresponding sparse proposal flow can effectively be transformed into a conventional dense flow field. We introduce two new challenging datasets that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use these benchmarks to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.Comment: arXiv admin note: text overlap with arXiv:1511.0506

Proposal Flow

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.~Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that proposal flow can effectively be transformed into a conventional dense flow field. We introduce a new dataset that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use this benchmark to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings

Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals

This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class. This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201

Unsupervised Object Discovery and Tracking in Video Collections

This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision. We formulate the problem as a combination of two complementary processes: discovery and tracking. The first one establishes correspondences between prominent regions across videos, and the second one associates successive similar object regions within the same video. Interestingly, our algorithm also discovers the implicit topology of frames associated with instances of the same object class across different videos, a role normally left to supervisory information in the form of class labels in conventional image and video understanding methods. Indeed, as demonstrated by our experiments, our method can handle video collections featuring multiple object classes, and substantially outperforms the state of the art in colocalization, even though it tackles a broader problem with much less supervision

A Data Analysis Methodology for Process Diagnosis and Redesign in Healthcare

Department of Management EngineeringDespite the disruptive and continuous development of healthcare environments, it still faces numerous challenges. Many of these are connected to clinical processes within the healthcare environment, which can be resolved through process analysis. At the same time, through the digitalization of healthcare, information from the various stakeholders in hospitals can be collected and stored in hospital information systems. On the basis of this stored data, evidence-based healthcare is possible, and this data-driven approach has become key to resolving medical issues. However, a more systematic data analysis methodology that covers the diagnosis and the redesign of clinical processes is required. Process mining, which aims to derive knowledgeable process-related insights from event logs, is a promising data-driven approach that is commonly used to address the challenges in healthcare. In other words, process mining has become a way to improve business process management in healthcare. For this reason, there have been numerous studies on clinical process analysis using process mining. However, these have mainly focused on investigating challenges facing clinical processes and have not reached a virtuous cycle until process improvement. Thus, a comprehensive data analysis framework for process diagnosis and redesign in healthcare is still required. \ud We identify three challenges in this research: 1) a lack of guidelines for data analysis to help understand clinical processes, 2) the research gap between clinical data analysis and process redesign in healthcare, and 3) a lack of accuracy and reliability in redesign assessment in healthcare. Based on these problem statements, this doctoral dissertation focuses on a comprehensive data analysis methodology for process diagnosis and redesign in healthcare. In particular, three frameworks are established to address important research issues in healthcare: 1) a framework for diagnosing clinical processes for outpatients, inpatients, and clinical pathways, 2) a framework for redesigning clinical processes with a simulation-based approach, and 3) a framework for evaluating the effects of process redesign. The proposed methodology has four steps: data preparation, data preprocessing, data analysis, and post-hoc analysis. The data preparation phase aims to extract data in a suitable format (i.e., event logs) for process mining data analysis. In this step, a method for obtaining clinical event logs from electronic health record data mapped using the common data model needs to be developed. To this end, we build an event log specification that can be used to derive event logs that consider the purpose, content, and scope of the data analysis desired by the user. After compiling the event logs, they are preprocessed to improve the accuracy and validity of the data analysis. The data analysis phase, which is the core component of the proposed methodology, consists of three components for process mining analysis: clinical process types, process mining types, and clinical perspectives. In the last phase, we interpret the results obtained from the data analysis with domain experts and perform a post-hoc analysis to improve clinical processes using simulations and to evaluate the previous data analysis results. For the first research issue, we propose a data analysis framework for three clinical process types: outpatients, inpatients, and clinical pathways. For each category, we provide a specific goal and include suitable fine-grained techniques in the framework which are either newly developed or based on existing approaches. We also provide four real-life case studies to validate the usefulness of this approach. For the second research issue, we develop a data-driven framework in order to build a discrete event simulation model. The proposed framework consists of four steps: data preparation and preprocessing, data analysis, post-hoc analysis, and further analysis. Here, we propose a mechanism for obtaining simulation parameters from process mining analysis from a control flow and performance perspective and automatically build a reliable and robust simulation model based on these parameters. This model includes realistic arrival rates and service times in a clinical setting. The proposed framework is constructed with a specific goal in mind (e.g., a decrease in waiting times), and the applicability of the framework is validated with a case study. For the final research issue, we develop a framework for evaluating the effects of process redesign. Two types of indicators are used for this: best practice implementation indicators to assess whether a specific best practice has been applied well or not and process performance indicators to understand the impact of the application of best practices. These indicators are explicitly connected to process mining functionalities. In other words, we provide a comprehensive method for assessing these indicators using clinical event logs. The usefulness of the methodology is demonstrated with real-life logs before and after a redesign. Compared to other existing frameworks in healthcare, this research is unique in constructing a healthcare-oriented data analysis methodology, rather than a generic model, that covers redesign in addition to diagnosis and in providing concrete analysis methods and data. As such, it is believed that this research will act as a motivation to extend the use of process mining in healthcare and will serve as a practical guideline for analyzing and improving clinical processes for non-experts.clos

Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope

Bayesian optimization is a popular method for solving the problem of global optimization of an expensive-to-evaluate black-box function. It relies on a probabilistic surrogate model of the objective function, upon which an acquisition function is built to determine where next to evaluate the objective function. In general, Bayesian optimization with Gaussian process regression operates on a continuous space. When input variables are categorical or discrete, an extra care is needed. A common approach is to use one-hot encoded or Boolean representation for categorical variables which might yield a {\em combinatorial explosion} problem. In this paper we present a method for Bayesian optimization in a combinatorial space, which can operate well in a large combinatorial space. The main idea is to use a random mapping which embeds the combinatorial space into a convex polytope in a continuous space, on which all essential process is performed to determine a solution to the black-box optimization in the combinatorial space. We describe our {\em combinatorial Bayesian optimization} algorithm and present its regret analysis. Numerical experiments demonstrate that our method outperforms existing methods.Comment: 10 pages, 2 figure