Approximating Condorcet Ordering for Vector-valued Mathematical Morphology
This addresses a domain-specific problem in image processing for vector-valued data, but it appears incremental as it builds on existing ordering concepts.
The paper tackles the lack of consensus on vector ordering for mathematical morphology in vector-valued images by developing a machine learning approach to learn a reduced ordering that approximates the Condorcet ordering, with preliminary experiments confirming its effectiveness for color images.
Mathematical morphology provides a nonlinear framework for image and spatial data processing and analysis. Although there have been many successful applications of mathematical morphology to vector-valued images, such as color and hyperspectral images, there is still no consensus on the most suitable vector ordering for constructing morphological operators. This paper addresses this issue by examining a reduced ordering approximating the Condorcet ranking derived from a set of vector orderings. Inspired by voting problems, the Condorcet ordering ranks elements from most to least voted, with voters representing different orderings. In this paper, we develop a machine learning approach that learns a reduced ordering that approximates the Condorcet ordering. Preliminary computational experiments confirm the effectiveness of learning the reduced mapping to define vector-valued morphological operators for color images.