Nowadays, the design of the representation of images is one of the most crucial factors in the performance of visual categorization. A common pipeline employed in most of recent researches for obtaining an image representation consists of two steps: the encoding step and the pooling step. In this paper, we introduce the Mahalanobis metric to the two popular image patch encoding modules, Histogr...

To classify time series by nearest neighbors, we need to specify or learn one or several distance measures. We consider variations of the Mahalanobis distance measures which rely on the inverse covariance matrix of the data. Unfortunately — for time series data — the covariance matrix has often low rank. To alleviate this problem we can either use a pseudoinverse, covariance shrinking or limit ...

The bootstrap error -adjusted s ingle-sample technique IBESTl is shown to per form bet ter than the Mahalanobis d is tance met ric in qualitative near-M analysis. The BEST algorithm is designed for high-speed parallel processing supercomputers, but is also shown to operate efficiently on sing le processors. Using hypothet ica l multivariate data, the bias and RSD of the BEST and Mahalanobis met...

We propose to learn multiple local Mahalanobis distance metrics to perform knearest neighbor (kNN) classification of temporal sequences. Temporal sequences are first aligned by dynamic time warping (DTW); given the alignment path, similarity between two sequences is measured by the DTW distance, which is computed as the accumulated distance between matched temporal point pairs along the alignme...

The Mahalanobis distances have been introduced in habitat selection studies for the estimation of environmental suitability maps (ESMs). The pixels of raster maps of a given area correspond to points in the multidimensional space defined by the mapped environmental variables (ecological space). The Mahalanobis distances measure the distances in this space between these points and the mean of th...

The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitionin...

Distance-based record linkage (DBRL) is a common approach to empirically assessing the disclosure risk in SDC-protected microdata. Usually, the Euclidean distance is used. In this paper, we explore the potential advantages of using the Mahalanobis distance for DBRL. We illustrate our point for partially synthetic microdata and show that, in some cases, Mahalanobis DBRL can yield a very high re-...

This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction i...

In previous work, semi-supervised Fuzzy c-means (ssFCM) was used as an automatic classification technique to classify the Nottingham Tenovus Breast Cancer (NTBC) dataset as no method to do this currently exists. However, the results were poor when compared with semi-manual classification. It is known that the NTBC data is highly non-normal and it was suspected that this affected the poor result...