(x − x)A(x − x) Mahalanobis distance: Previous work on learning metrics has focused on learning a single distance metric for all instances. One of our primary contributions is to learn a distance function for every training image. Most visual categorization approaches make use of machine learning after computing distances between images (e.g. SVM with pyramid kernel). We want to learn how to co...

The scaling parameter α helps maintain a balance between supervised and unsupervised learning in semi-supervised Fuzzy c-Means (ssFCM). In this study, we investigated the effects of different α values, 0.1, 0.5, 1 and 10 in Pedrycz and Waletsky’s ssFCM with various amounts of labelled data, 10%, 20%, 30%, 40%, 50% and 60% and three distance metrics, Euclidean, Mahalanobis and kernel-based on th...

In this paper, we consider the problem of feature selection and classifier fusion and discuss how they should be reflected in the fusion system architecture. We employed the genetic algorithm with a novel coding to search the worst performing fusion strategy. The proposed algorithm tunes itself between feature and matching score levels, and improves the final performance over the original on tw...

The Mahalanobis distance is commonly used in multi-object trackers for measurement-to-track association. Starting with the original definition of the Mahalanobis distance we review its use in association. Given that there is no principle in multi-object tracking that sets the Mahalanobis distance apart as a distinguished statistical distance we revisit the global association hypotheses of multi...

Aiming at solving lengthy time-consuming registration and mismatch problems caused by images which have feature simliarity of local region in the traditional SIFT (Scale Invariant Feature Transform) registration method, a fine registration method based on geometric constraints and parallel architecture is put forward. First, in order to provide geometric constraints for block and fine registrat...

The fundamental techniques used for man-machine communication include Speech synthesis, speech recognition, and speech transformation. Feature extraction techniques provide a compressed representation of the speech signals. The HNM analyses and synthesis provides high quality speech with less number of parameters. Dynamic time warping is well known technique used for aligning two given multidim...

Title of Document: DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE Sachin Kumar, Doctor of Philosophy (Ph.D.), 2009 Directed By: Chair Professor, Michael Pecht, Department of Mechanical Engineering Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and H...

In face recognition feature extraction and classification are the two aspects to be focused. In principle component analysis (PCA) based face recognition technique, the 2D face image matrices must be previously transformed in to one dimensional image vectors. In this paper two dimensional principle component analysis(2DPCA) is used to extract the features. Comparing to conventional principle co...

Segmentation is an important step for image analysis, but a good segment algorithm which can handle color image with texture area and has less computation time is rare. W e propose to use the local window image histogram, which is easy to compute and could quickly collect the information of neighbors, together with the Se2f-Organization of Kohonen Feature Map ( SOFM ) neural network, which can ...