Shape contexts enable efficient retrieval of similar shapes

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Shape contexts enable efficient retrieval of similar shapes. / Mori, Greg; Belongie, Serge; Malik, Jitendra.

In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2001, p. I723-I730.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Mori, G, Belongie, S & Malik, J 2001, 'Shape contexts enable efficient retrieval of similar shapes', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I723-I730.

APA

Mori, G., Belongie, S., & Malik, J. (2001). Shape contexts enable efficient retrieval of similar shapes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, I723-I730.

Vancouver

Mori G, Belongie S, Malik J. Shape contexts enable efficient retrieval of similar shapes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001;1:I723-I730.

Author

Mori, Greg ; Belongie, Serge ; Malik, Jitendra. / Shape contexts enable efficient retrieval of similar shapes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001 ; Vol. 1. pp. I723-I730.

Bibtex

@inproceedings{20b0e82160d5408e838fd0c48ec2b61f,
title = "Shape contexts enable efficient retrieval of similar shapes",
abstract = "In this work we demonstrate that a recently introduced shape descriptor, the {"}shape context{"}, can be used to quickly prune a search for similar shapes. Our representation for a shape is a discrete set of n points sampled from its internal and external contours. For each of these points, the shape context is a histogram of the relative positions of the n - 1 remaining points. We present two methods for rapid shape retrieval: one that does comparisons based on a small number of shape contexts and another that uses vector quantization in the space of shape contexts. We verify the discriminative power of these methods with tests on the Columbia (COIL-100) 3D object database and the Snod-grass and Vanderwart line drawings. The shape context-based methods are shown to quickly produce an accurate shortlist of candidates suitable for a more exact matching engine in spite of pose variation and occlusion.",
author = "Greg Mori and Serge Belongie and Jitendra Malik",
year = "2001",
language = "English",
volume = "1",
pages = "I723--I730",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",
note = "2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ; Conference date: 08-12-2001 Through 14-12-2001",

}

RIS

TY - GEN

T1 - Shape contexts enable efficient retrieval of similar shapes

AU - Mori, Greg

AU - Belongie, Serge

AU - Malik, Jitendra

PY - 2001

Y1 - 2001

N2 - In this work we demonstrate that a recently introduced shape descriptor, the "shape context", can be used to quickly prune a search for similar shapes. Our representation for a shape is a discrete set of n points sampled from its internal and external contours. For each of these points, the shape context is a histogram of the relative positions of the n - 1 remaining points. We present two methods for rapid shape retrieval: one that does comparisons based on a small number of shape contexts and another that uses vector quantization in the space of shape contexts. We verify the discriminative power of these methods with tests on the Columbia (COIL-100) 3D object database and the Snod-grass and Vanderwart line drawings. The shape context-based methods are shown to quickly produce an accurate shortlist of candidates suitable for a more exact matching engine in spite of pose variation and occlusion.

AB - In this work we demonstrate that a recently introduced shape descriptor, the "shape context", can be used to quickly prune a search for similar shapes. Our representation for a shape is a discrete set of n points sampled from its internal and external contours. For each of these points, the shape context is a histogram of the relative positions of the n - 1 remaining points. We present two methods for rapid shape retrieval: one that does comparisons based on a small number of shape contexts and another that uses vector quantization in the space of shape contexts. We verify the discriminative power of these methods with tests on the Columbia (COIL-100) 3D object database and the Snod-grass and Vanderwart line drawings. The shape context-based methods are shown to quickly produce an accurate shortlist of candidates suitable for a more exact matching engine in spite of pose variation and occlusion.

UR - http://www.scopus.com/inward/record.url?scp=0035683817&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0035683817

VL - 1

SP - I723-I730

JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

SN - 1063-6919

T2 - 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Y2 - 8 December 2001 through 14 December 2001

ER -

ID: 302058751