The minimum distance classifier mdc 14 is used in various areas of pattern recognition because it is simple and fast compared with other complicated classifiers. Estimate class mean vector and covariance matrix from training. Minimum distance to means classification algorithm the minimum distance to means decision rule is computationally simple and commonly used. I, i is the unit matrix then the mahalanobis distance reduces to a scaled version of the euclidean distance. A fast hybrid classification algorithm based on the minimum distance and the knn classifiers conference paper pdf available january 2011 with 54 reads how we measure reads. Euclidean and mahalanobis classifiers always return same. More precisely, in the minimum distance algorithm, there are two such parameters. Display the input file you will use for minimum distance classification, along with the roi file. The discriminate function is defined in terms of distance from the mean. Pdf a fast algorithm for the minimum distance classifier. The reason why there is no difference in classification lies in your covariance matrix. The maximin maximum minimum algorithm represents an attempt to define a less arbitrary and more repeatable distribution of cluster groups. The minimum distance classifier defines classes in terms of the distance from a prototype vector usually the mean vector for the class. Take a new unknown example and put it into the correct class using discriminant function.
The minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in multi. In the next lines i will 1 explain why minimum distance classifier can perform well on. Pdf a fast hybrid classification algorithm based on the. Suppose that each training class is represented by a prototype or mean vector. Supervised image classification using minimum distance. From the toolbox, select classification supervised classification minimum distance classification. The simple clustering algorithm described above is extremely sensitive to the both the threshold distance, t, and the order in which pixels are selected from the image. For example, 25 clusters 250 observation in train set gave me 93. This paper presents a new solution for choosing the k parameter in the knearest neighbor knn algorithm, the solution depending on the idea of ensemble learning, in which a weak knn classifier. Several minimumdistance algorithms for identifying the nearest. Mahalanobis distance reduces the euclidean distance according to the extent of variation within the data, given by the. For a, the minimum distance classi fier performance is typically 5% to. The mahalanobis distance between two multivariate quantities x and y is if the covariance matrix is k.