Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Automatic texture feature selection for image pixel classification
Pattern Recognition
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Autonomous science for an ExoMars Rover–like mission
Journal of Field Robotics - Special Issue on Space Robotics, Part II
Template Matching Techniques in Computer Vision: Theory and Practice
Template Matching Techniques in Computer Vision: Theory and Practice
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Wavelet Feature Selection for Image Classification
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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This paper presents a novel application of advanced machine learning techniques for Mars terrain image classification. Fuzzy-rough feature selection (FRFS) is adapted and then employed in conjunction with Support Vector Machines (SVMs) to construct image classifiers. These techniques are integrated to address problems in space engineering where the images are of many classes, large-scale, and diverse representational properties. The use of the adapted FRFS allows the induction of low-dimensionality feature sets from feature patterns of a much higher dimensionality. To evaluate the proposed work, K-Nearest Neighbours (KNNs) and decision trees (DTREEs) based image classifiers as well as information gain rank (IGR) based feature selection are also investigated here, as possible alternatives to the underlying machine learning techniques adopted. The results of systematic comparative studies demonstrate that in general, feature selection improves the performance of classifiers that are intended for use in high dimensional domains. In particular, the proposed approach helps to increase the classification accuracy, while enhancing classification efficiency by requiring considerably less features. This is evident in that the resultant SVM-based classifiers which utilise FRFS-selected features generally outperform KNN and DTREE based classifiers and those which use IGR-returned features. The work is therefore shown to be of great potential for on-board or ground-based image classification in future Mars rover missions.