@inproceedings{e0a9110fc5b4458f88fbbf7ddcfab0b6,
title = "Image specific feature similarities",
abstract = "Calculating a reliable similarity measure between pixel features is essential for many computer vision and image processing applications. We propose a similarity measure (affinity) between pixel features, which depends on the feature space histogram of the image. We use the observation that clusters in the feature space histogram are typically smooth and roughly convex. Given two feature points we adjust their similarity according to the bottleneck in the histogram values on the straight line between them. We call our new similarities Bottleneck Affinities. These measures are computed efficiently, we demonstrate superior segmentation results compared to the use of the Euclidean metric.",
author = "Ido Omer and Michael Werman",
year = "2006",
doi = "10.1007/11744047_25",
language = "אנגלית",
isbn = "3540338349",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "321--333",
booktitle = "Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings",
note = "9th European Conference on Computer Vision, ECCV 2006 ; Conference date: 07-05-2006 Through 13-05-2006",
}