Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Rotated texture classification by improved iterative morphological decomposition

Abstract: An improved algorithm based on iterative morphological decomposition (IMD) proposed by Wang et al. (1993) is described. The proposed algorithm requires less computation than the original IMD algorithm. The improved iterative morphological decomposition (IIMD) is compared with granulometric moments, multiresolution rotation-invariant SAR (MRRISAR) models and multichannel Gabor filters. It is found that IIMD is superior to granulometric moments and MRRISAR in rotated texture classification. IIMD may also perform better than multichannel Gabor filters by employing many different kinds of structuring elements. In the study, three kinds of pseudo rotation-invariant structuring elements, namely the disc, octagon and square, as well as a line structuring element are tested. Since the line structuring element is rotation-variant in nature, the image is rotated to different orientations of equal angular separation to find a set of primitive features. A Fourier transform is then applied to convert these features to rotation-invariant. An accuracy rate as high as 96% is achieved in classifying 30 classes of textured images in the experiment. It is also demonstrated that using both the normalised variance and the mean can give better classification accuracy rate than using both the variance and the mean when classified by simplified Bayes or Mahalanobis distance measure.

Feature weighted mahalanobis distance: Improved robustness for Gaussian classifiers

Abstract: Gaussian classifiers are strongly dependent on their underlying distance method, namely the Mahalanobis distance. Even though widely used, in the presence of noise this distance measure loses dramatically in performance, due to equal summation of the squared distances over all features. The features with large distance can mask all the other features so that the classification considers only these features, neglecting the information provided by the other features. To overcome this drawback we propose to weight the different features in the Mahalanobis distance according to their distances after the variance normalization. The idea behind this is to give less weight to noisy features and high weight to noise free features which are more reliable. Thereafter, we replace the traditional distance measure in a Gaussian classifier with the proposed. In a series of experiments we show the improved noise robustness of Gaussian classifiers by the proposed modifications in contrast to the traditional approach.

Hyperspectral anomalous change detection based on joint sparse representation

Abstract: Anomalous change detection aims at finding small but unusual changes from the unchanged or generally changed background in multi-temporal hyperspectral remote sensing images It is important to model the spectral variations of background so as to highlight the anomalous changes In this paper, we proposed a hyperspectral anomalous change detection method based on joint sparse representation A background dictionary is constructed by the randomly selected pixels in the stacked multi-temporal images The local neighborhood pixels surrounding the test pixel are presented by joint sparse representation with the background dictionary Thus, the change tendencies in the local background are modeled by the active dictionary bases The difference of separate reconstruction coefficients of the test pixel with the active bases will reflect the probability to be anomalously changed Three detectors, which are coefficient difference, Mahalanobis distance of coefficient difference and multi-temporal residual analysis, are proposed to measure the change intensity Two experiments with the datasets of “Viareggio 2013 Trial” and one Hyperion indicate that the proposed method obtains better performances than the comparative methods

Health monitoring of hard disk drive based on Mahalanobis distance

Abstract: A hard disk drive (HDD) is one of the core components of most computer systems. A failure of HDD may cause serious data loss and catastrophic consequences. Thus, health monitoring and anomaly prediction for HDD are critical to prevent data loss and make strategies for data backup. This paper analyzed the potential failure modes and failure mechanisms influencing on HDD reliability by FMMEA (Failure Modes, Mechanisms and Effects Analysis) method and performed the prioritization by estimating the risk priority numbers. The Head Disk Interface (HDI) and head stack assembly related failure and relevant performance parameters are identified as the dominant failure mode and health monitoring parameters. A novel strategy for anomaly prediction of hard disk based on Mahalanobis distance using SMART attributes is also suggested in this paper. Furthermore, a case study of HDD anomaly prediction based on the methodology presented in this paper is carried out. The experiment results showed that the proposed method is feasible.