Mahalanobis distance

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

Automated processing of coral reef benthic images

Abstract: We describe an automated computer algorithm for the classification of coral reef benthic organisms and substrates sampled using a typical photographic quadrat survey. The technique compares subsections of a quadrat sample image (blocks) to a library of identified species blocks and computes a distance or probability of identification in a multidimensional hypervolume of discrimination metrics. The discrimination metrics include texture (calculated from a radial sampling of a two-dimensional discrete cosine transform) and three channels of a normalized color space. A standard multivariate classification technique based on the Mahalanobis distance was unsuccessful in discriminating substrata because of the large morphological variation inherent in reef organisms. An alternative classification scheme based on an exhaustive search through an organism reference library yielded classification maps comparable to those obtained by manual analysis.

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Abstract: Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models. Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

Operational map-guided classification of SAR sea ice imagery

Abstract: This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual "egg code" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71% and 89%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

Analysis of euclidean distance and manhattan distance measure in face recognition

Abstract: The face expression recognition problem is challenging because different individuals display the same expression differently [1].Here PCA algorithm is used for the feature extraction. Distance metric or matching criteria is the main tool for retrieving similar images from large image databases for the above category of search. Two distance metrics, such as the L1 metric (Manhattan Distance), the L2 metric (Euclidean Distance) have been proposed in the literature for measuring similarity between feature vectors. In content-based image retrieval systems, Manhattan distance and Euclidean distance are typically used to determine similarities between a pair of image [2]. Here facial images of three subjects with different expression and angles are used for classification. Experimental results are compared and the results show that the Manhattan distance performs better than the Euclidean Distance.