Showing papers by "Reid B. Porter published in 2015"

Knowledge-based probabilistic logic learning

Abstract: Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.

Learning Watershed Cuts Energy Functions

Abstract: In recent work, several popular segmentation methods have been unified as energy minimization on a graph. In other work, supervised learning methods have been generalized from predicting labels to predicting structured, graph-like objects. A recent contribution to this second area showed how the Rand Index could be directly minimized when using Connected Components as a segmentation method. We build on this work and present an efficient mini-batch learning method for Connected Component segmentation and also show how it can be generalized to the Watershed Cuts segmentation method. We present initial results applying these new contributions to image segmentation problems in materials microscopy and discuss challenges and future directions.

Segmentation and learning in the quantitative analysis of microscopy images

Abstract: In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

Links between binary classification and the assignment problem in ordered hypothesis machines

Abstract: Ordered Hypothesis Machines (OHM) are large margin classifiers that belong to the class of Generalized Stack Filters which were originally developed for non-linear signal processing. In previous work we showed how OHM classifiers are equivalent to a variation of Nearest Neighbor classifiers, with the advantage that training involves minimizing a loss function which includes a regularization parameter that controls class complexity. In this paper we report a new connection between OHM training and the Linear Assignment problem, a combinatorial optimization problem that can be solved efficiently with (amongst others) the Hungarian algorithm. Specifically, for balanced classes, and particular choices of parameters, OHM training is the dual of the Assignment problem. The duality sheds new light on the OHM training problem, opens the door to new training methods and suggests several new directions for research.

Discovering compositional trends in Mars rock targets from ChemCam spectroscopy and remote imaging

Abstract: Onboard the Mars rover “Curiosity”, ChemCam contains two instruments that gather geological data in the form of remote micro images (RMI) for geologic context and laser-induced breakdown spectroscopy (LIBS) for chemical composition. By analyzing the geochemical compositional depth trends of rocks, surface layers are identified that provide clues to the past atmospheric and aqueous conditions of the planet. LIBS produces the necessary data of chemical depth profiles with successive laser shots. To quickly identify these surface layers, we fit a Gaussian graphical model (GGM) to LIBS depth profiles on rock targets. The learned GGM is a visual representation of conditional dependencies among the set of shots making for faster identification of targets with interesting depth trends that warrant more in-depth analysis by experts. We show that our learned GGMs reveal information about the compositional trends present in rock targets that match observations made in more focused studies on these same targets. RMI images provide complementary details about the rock surface. Using RMI and LIBS features, we can cluster similar rock targets by the properties of the rock's surface texture and depth profile. We present results that show our machine learning methods can help analyze both the breadth and depth of data collected by ChemCam.