Texture analysis and discrimination in additive noise
Computer Vision, Graphics, and Image Processing
Vector quantization and signal compression
Vector quantization and signal compression
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Parallel k/h-Means Clustering for Large Data Sets
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
SIAM Journal on Matrix Analysis and Applications
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Journal of Field Robotics - Special Issue on Space Robotics, Part III
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Automatic detection of dust devils and clouds on Mars
Machine Vision and Applications
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
k-Means Has Polynomial Smoothed Complexity
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Least squares quantization in PCM
IEEE Transactions on Information Theory
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists’ labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.