Matching Handwritten Line Drawings with Von Mises Distributions

Katsutoshi UEAOKI; Kazunori IWATA; Nobuo SUEMATSU; Akira HAYASHI

doi:10.1587/transinf.E94.D.2487

Matching Handwritten Line Drawings with Von Mises Distributions

Katsutoshi UEAOKI, Kazunori IWATA, Nobuo SUEMATSU, Akira HAYASHI

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A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.

Publication: IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2487-2494

Publication Date: 2011/12/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E94.D.2487

Type of Manuscript: PAPER

Category: Pattern Recognition

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Katsutoshi UEAOKI, Kazunori IWATA, Nobuo SUEMATSU, Akira HAYASHI, "Matching Handwritten Line Drawings with Von Mises Distributions" in IEICE TRANSACTIONS on Information, vol. E94-D, no. 12, pp. 2487-2494, December 2011, doi: 10.1587/transinf.E94.D.2487.
Abstract: A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2487/_p

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@ARTICLE{e94-d_12_2487,
author={Katsutoshi UEAOKI, Kazunori IWATA, Nobuo SUEMATSU, Akira HAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Matching Handwritten Line Drawings with Von Mises Distributions},
year={2011},
volume={E94-D},
number={12},
pages={2487-2494},
abstract={A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.},
keywords={},
doi={10.1587/transinf.E94.D.2487},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - Matching Handwritten Line Drawings with Von Mises Distributions
T2 - IEICE TRANSACTIONS on Information
SP - 2487
EP - 2494
AU - Katsutoshi UEAOKI
AU - Kazunori IWATA
AU - Nobuo SUEMATSU
AU - Akira HAYASHI
PY - 2011
DO - 10.1587/transinf.E94.D.2487
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - A two-dimensional shape is generally represented with line drawings or object contours in a digital image. Shapes can be divided into two types, namely ordered and unordered shapes. An ordered shape is an ordered set of points, while an unordered shape is an unordered set. As a result, each type typically uses different attributes to define the local descriptors involved in representing the local distributions of points sampled from the shape. Throughout this paper, we focus on unordered shapes. Since most local descriptors of unordered shapes are not scale-invariant, we usually make the shapes in an image data set the same size through scale normalization, before applying shape matching procedures. Shapes obtained through scale normalization are suitable for such descriptors if the original whole shapes are similar. However, they are not suitable if parts of each original shape are drawn using different scales. Thus, in this paper, we present a scale-invariant descriptor constructed by von Mises distributions to deal with such shapes. Since this descriptor has the merits of being both scale-invariant and a probability distribution, it does not require scale normalization and can employ an arbitrary measure of probability distributions in matching shape points. In experiments on shape matching and retrieval, we show the effectiveness of our descriptor, compared to several conventional descriptors.
ER -