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Subjects/Areas/Topics:Applications
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Computer Vision, Visualization and Computer Graphics
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Image Understanding
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Pattern Recognition
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Spectral Methods
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Theory and Methods

Abstract: In many image-segmentation algorithms, measuring the distances is a key problem since the distance is often used to decide whether two image points belong to a single or, respectively, to two different image segments. The usual Euclidean distance need not be the best choice. Measuring the distances along the surface that is defined by the image function seems to be more relevant in more complicated images. Geodesic distance, i.e. the shortest path in the corresponding graph, or the k shortest paths can be regarded as the simplest methods. It might seem that the diffusion distance should provide the properties that are better since all the paths (not only their limited number) are taken into account. In this paper, we firstly show that the diffusion distance has the properties that make it difficult to use it image segmentation, which extends the recent observations of some other authors. Afterwards, we propose a new measure called normalised diffusion cosine similarity that is more suitable. We present the corresponding theory as well as the experimental results.(More)

In many image-segmentation algorithms, measuring the distances is a key problem since the distance is often used to decide whether two image points belong to a single or, respectively, to two different image segments. The usual Euclidean distance need not be the best choice. Measuring the distances along the surface that is defined by the image function seems to be more relevant in more complicated images. Geodesic distance, i.e. the shortest path in the corresponding graph, or the k shortest paths can be regarded as the simplest methods. It might seem that the diffusion distance should provide the properties that are better since all the paths (not only their limited number) are taken into account. In this paper, we firstly show that the diffusion distance has the properties that make it difficult to use it image segmentation, which extends the recent observations of some other authors. Afterwards, we propose a new measure called normalised diffusion cosine similarity that is more suitable. We present the corresponding theory as well as the experimental results.

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Gaura, J. and Sojka, E. (2015). Normalised Diffusion Cosine Similarity and Its Use for Image Segmentation.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 121-129. DOI: 10.5220/0005220601210129

@conference{icpram15, author={Jan Gaura. and Eduard Sojka.}, title={Normalised Diffusion Cosine Similarity and Its Use for Image Segmentation}, booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,}, year={2015}, pages={121-129}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005220601210129}, isbn={978-989-758-076-5}, }

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, TI - Normalised Diffusion Cosine Similarity and Its Use for Image Segmentation SN - 978-989-758-076-5 AU - Gaura, J. AU - Sojka, E. PY - 2015 SP - 121 EP - 129 DO - 10.5220/0005220601210129