Transformation-Based Fuzzy Rule Interpolation With Mahalanobis Distance Measures Supported by Choquet Integral

Fuzzy rule interpolation (FRI) strongly supports approximate inference when a new observation matches no rules, through selecting and subsequently interpolating appropriate rules close to the observation from the given (sparse) rule base. Traditional ways of implementing the critical rule selection process are typically based on the exploitation of Euclidean distances between the observation and rules. It is conceptually straightforward for implementation but applying this distance metric may systematically lead to inferior results because it fails to reflect the variations of the relevance or significance levels amongst different domain features. To address this important issue, a novel transformation-based FRI approach is presented, on the basis of utilising the Mahalanobis distance metric. The new FRI method works by transforming a given sparse rule base into a coordinates system where the distance between instances of the same category becomes closer while that between different categories becomes further apart. In so doing, when an observation is present that matches no rules, the most relevant neighbouring rules to implement the required interpolation are more likely to be selected. Following this, the scale and move factors within the classical transformation-based FRI procedure are also modified by Choquet integral. Systematic experimental investigation over a range of classification problems demonstrates that the proposed approach remarkably outperforms the existing state-of-the-art FRI methods in both accuracy and efficiency

Fuzzy Connectives for Efficient Image Reduction and Speeding Up Image Analysis

We discuss non-monotone fuzzy connectives in large scale image processing. We present an image reduction algorithm capable of differentiating between fine image details and noise in the image, particularly salt and pepper noise. The reduction algorithm is based on mode-like averaging functions. We compare the performance of the proposed method to the alternative reduction methods on artificial images and on two case studies: content based image retrieval and pedestrian detection. Our algorithm improves the speed of the subsequently applied image analysis methods and allows efficient filtering of salt and pepper noise. Applications to on-board image recognition in autonomous robotic devices are envisaged