We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single- and multi-channel distance measures between information-rich image/object representations.
CITATION STYLE
Öfverstedt, J., Sladoje, N., & Lindblad, J. (2017). Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10225 LNCS, pp. 395–407). Springer Verlag. https://doi.org/10.1007/978-3-319-57240-6_32
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