Euclidean distance measure has been used in comparing feature vectors of images, while cosine angle distance measure is used in document retrieval. In this paper, we theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of color image database. We find that the cosine angle distance, in general, works equally ...

Abstract The distance matrix

This paper presents a new approach for human identification at a distance using gait recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four view directions to silhouette. Based on normalized correlation...

Euclidean distance matrices (EDM) are symmetric nonnegative with several interesting properties. In this article, we introduce a wider class of called generalized (GEDMs) that include EDMs. Each GEDM is an entry-wise matrix. A not unless it EDM. By some new techniques, show many significant results on can be extended to matrices. These contain about eigenvalues, inverse, determinant, spectral r...

In this paper we provide a novel measure based on direct Hausdorff distance (DHD). Most researchers have used the Euclidean distance (EUD) or DHD. We propose the use of normalized cosine distance (COSD) and EUD as finite set points instead of a set of image pixels. The proposed measure takes into account the integration of global and local features. For the performance assessment a genetic algo...