Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

A method and apparatus for the detection of local image structures represented as clusters in a joint-spatial range domain where the method comprises receiving an input image made having one or more clusters in a joint-spatial range domain, and each of the one or more clusters having a corresponding mode. Receiving a set of analysis matrices and selecting through each one of the analysis matrices. Using the selected analysis matrix to partition the input image into the one or more clusters and their corresponding modes, and computing a mean, μ, and a local covariance matrix Σ for each of the corresponding modes of each of the one or more clusters. Selecting at least one of the one or more clusters, where each selected cluster has a stable mean and stable covariance matrix across the set of analysis matrices, whereby each of the selected clusters is indicative of a local image structure.

Multiscale detection of local image structures

Accurate characterization of data distribution is of significant importance for vision problems. In many situations, multivariate visual data often spread into a nonlinear manifold in the high-dimensional space, which makes traditional linear modeling techniques ineffective. This paper proposes a generic nonlinear modeling scheme based on parametric data representations. We build a compact representation for the visual data using a set of parameterized basis (wavelet) functions, where the parameters are randomized to characterize the nonlinear structure of the data distribution. Meanwhile, a new progressive density approximation scheme is proposed to obtain an accurate estimate of the probability density, which imposes discrimination power on the model. Both synthetic and real image data are used to demonstrate the strength of our modeling scheme.

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Parametric representations for nonlinear modeling of visual data

There is a high demand for patient specific cardiovascular therapeutics, especially in pediatric cardiology which is confronted with complex and rather unique congenital diseases. Current predictors for disease severity and treatment selection have been proven to be suboptimal creating profound burden of premature morbidity and mortality. Over the past decade, the influence of blood hemodynamics has become increasingly acknowledged, especially in the context of congenital diseases of the aortic arch. MRI-based 2D and 3D flow measurements are nowadays possible, although restricted by cumbersome acquisition protocols and limited acquisition resolution. Computational fluid dynamics (CFD) offers a valuable alternative that also enables treatment outcome prediction. However, the current methods rely on a sequence of complicated manual steps that lack the scalability required within clinical settings. We propose a computation framework for large-scale hemodynamics simulations in pediatric cardiology to aid diagnostic and therapy decision making in patients affected by congenital disease of the aortic valve (AV) and the aorta. Our method provides a deterministic and streamlined processing pipeline to perform CFD simulations based on patient-specific boundary conditions. Thus, blood flow simulations are performed using an embedded boundary method within a level-set formulation with boundary conditions provided by patient-specific anatomical and hemodynamical models. The capabilities of our framework are demonstrated by performing blood flow analysis on patients selected from an FDA-sponsored multicenter clinical trial.

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Computational Fluid Dynamics Framework for Large-Scale Simulation in Pediatric Cardiology

A method for determining a volume of interest in data includes determining fixed-bandwidth estimations of a plurality of analysis bandwidths, wherein the estimation of the fixed-bandwidth comprises, providing an estimate of a mode location of the volume of interest in the data, and determining a covariance of the volume of interest using a local Hessian matrix. The method further includes determining the volume of interest as a most stable fixed-bandwidth estimation across each of the plurality of analysis bandwidths.

Volumetric characterization using covariance estimation from scale-space hessian matrices

This paper introduces the concept of training set synthesis for entropy-constrained transform vector quantization (TSS-ECTVQ). The statistics of actual sets of transform vectors are first approximated using histograms. New sets of vectors-called synthesized training sets-are obtained based upon the estimated parameters-called training set parameters. By employing a fast entropy-constrained algorithm, codebooks are populated from the synthesized training sets for each image being coded. Then, entropy-constrained vector quantization is performed. The training set parameters are sent to the decoder, which obtains the same training sets, and generates codebooks identical to the encoder. Experimental results demonstrate that high quality image coding at low bit rates can be obtained with the proposed TSS-ECTVQ method. In particular, the image Lenna was coded at 0.25 bits/pixel with a PSNR of 32.42 dB.

Dorin Comaniciu

Papers

Multiscale detection of local image structures

Parametric representations for nonlinear modeling of visual data

Computational Fluid Dynamics Framework for Large-Scale Simulation in Pediatric Cardiology

Volumetric characterization using covariance estimation from scale-space hessian matrices

Training set synthesis for entropy-constrained transform vector quantization