Dissimilarity Measures in Color Spaces
نویسندگان
چکیده
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 are compared in terms of sensitivity to radiometric variations, spatial shifts and shape distortions. We compare the results with those obtained by subjective testing.
منابع مشابه
Does Color Really Help in Dense Stereo Matching
This paper investigates the role of color in global stereo matching approaches. In our evaluation study, we build various energy functions by combining nine color spaces with four dissimilarity functions and test their performance on 30 ground truth stereo pairs. Our experiments start by computing the matching scores via the absolute difference of color values. As is consistent with previous st...
متن کاملAdaptive Binning and Dissimilarity Measure for Image Retrieval and Classification
Color histogram is an important part of content-based image retrieval systems. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than histograms with fixed binnings. However, among existing dissimilarity measures, only the Earth Mover’s Distance can compare histograms with different binnings. This paper presents a detailed...
متن کاملComparing Dissimilarity Measures for Content-Based Image Retrieval
Dissimilarity measurement plays a crucial role in contentbased image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performan...
متن کاملEmpirical Evaluation of Dissimilarity Measures for Color and Texture
This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color, and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, ...
متن کاملA New Similarity Measure for Random Signatures: Perceptually Modified Hausdorff Distance
In most content-based image retrieval systems, the low level visual features such as color, texture and region play an important role. Variety of dissimilarity measures were introduced for an uniform quantization of visual features, or a histogram. However, a cluster-based representation, or a signature, has proven to be more compact and theoretically sound for the accuracy and robustness than ...
متن کامل