Dissimilarity cumulation theory in arc-connected spaces

Dissimilarity Measures in Color Spaces

A problem of both theoretical and practical importance in image processing is to compare two color images, taking into account the eventual diversities due to translation, or color variations. The aim of this paper is to study the behavior of dissimilarity measures in different color spaces. Five color spaces (RGB, HSI, HSV, CIELab, CIELuv) are studied. Properties of the dissimilarity measures ...

Jet Spaces and Arc Spaces

Before getting into details, I would like to recall the p-adic inspiration for this theory. Here is a list of things we looked at in Chapter III. (1) Polynomials f ∈ Z[X1, . . . , Xn] and their solutions over Z/pZ ' Zp/pZp, where p is a fixed prime and m ≥ 0. Such a solution can be written as x1 = a10 + a11p+ . . .+ a1mp m , . . . , xn = an0 + an1p+ . . .+ anmp m with ai ∈ {0, . . . , p− 1}. (2...

Learning User Queries in Multimodal Dissimilarity Spaces

Different strategies to learn user semantic queries from dissimilarity representations of video audio-visual content are presented. When dealing with large corpora of videos documents, using a feature representation requires the online computation of distances between all documents and a query. Hence, a dissimilarity representation may be preferred because its offline computation speeds up the ...

Fast Nearest-Neighbor Search in Dissimilarity Spaces

Time Series Classification in Dissimilarity Spaces

Time series classification in the dissimilarity space combines the advantages of the dynamic time warping and the rich mathematical structure of Euclidean spaces. We applied dimension reduction using PCA followed by support vector learning on dissimilarity representations to 43 UCR datasets. Results indicate that time series classification in dissimilarity space has potential to complement the ...