Machine learning techniques have shown considerable promise for visual inspection tasks such as locating human faces in cluttered scenes. In this paper, we examine the utility of such techniques for the scientifically-important problem of detecting and catloging impact craters in planetary images gathered by spacecraft. Various supervised learning algorithms, including ensemble methods (bagging and AdaBoost with feed-forward neural networks as base learners), support vector machines (SVM), and continuouslyscalable template models (CSTM), are employed to derive crater detectors from ground-truthed images. The resulting detectors are evaluated on a challenging set of Viking Orbiter images of Mars containing roughly one thousand craters. The SVM approach with normalized image patches provides detection and localization performance closest to that of human labelers and is shown to be substantially superior to boundary-based approaches such as the Hough transform.

Efficient learning from Web resources can depend on accurately assessing the quality of each resource. We present a methodology for developing computational models of quality that can assist users in assessing Web resources. The methodology consists of four steps: 1) a meta-analysis of previous studies to decompose quality into high-level dimensions and low-level indicators, 2) an expert study to identify the key low-level indicators of quality in the target domain, 3) human annotation to provide a collection of example resources where the presence or absence of quality indicators has been tagged, and 4) training of a machine learning model to predict quality indicators based on content and link features of Web resources. We find that quality is a multifaceted construct, with different aspects that may be important to different users at different times. We show that machine learning models can predict this multifaceted nature of quality, both in the context of aiding curators as they evaluate resources submitted to digital libraries, and in the context of aiding teachers as they develop online educational resources. Finally, we demonstrate how computational models of quality can be provided as a service, and embedded into applications such as Web search.

Machine learning techniques have shown considerable promise for automating common visual inspection tasks such as the detection of human faces in cluttered scenes. Here, we examine whether similar techniques can be used (or adapted) for the problem of automatically locating geologic landforms in planetary images gathered by spacecraft. Beyond enabling more efficient and comprehensive ground analysis of down-linked data, we are aiming toward perceptive spacecraft that use onboard processing to autonomously analyze their collected imagery and take appropriate actions. In our current study, we have employed various supervised learning algorithms, including neural networks, ensemble methods, support vector machines (SVM), and continuously-scalable template models (CSTM) to derive detectors for craters from ground-truthed images. The resulting detectors are evaluated on a challenging set of Viking Orbiter images of Mars containing roughly one thousand craters. The SVM approach with normalized image patches provides detection and localization performance closest to that of human labelers and is shown to be substantially superior to boundary-based approaches such as the Hough transform. However, the run-time cost in applying the SVM solution in the standard way (spatial scanning in which the SVM is applied to each patch of the image on a window-by-window basis) is too high due both to the number of support vectors required and the number of test vectors generated by sliding a window across the data. We have developed an implementation using FFTs and the overlapand-add technique, which can be used to efficiently apply SVMs to sensor data in resourceconstrained environments such as on a spacecraft. The technique allows exact computation of the SVM decision function over an image using minimal RAM (typically less than 5% of the size of the image) and only O(n s (log 2 d + 11)) real multiplications per pixel, where n s Mach Learn (2011) 84:341-367 is the number of support vectors and d is the dimensionality of the vectors compared with O(n s d) real multiplications per pixel for spatial scanning. Our approach is complementary to reduced set methods providing (in theory) a multiplicative gain in performance.

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