Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation
Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, w...
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| Published in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 2657 - 2665 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
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01.01.2022
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| ISSN: | 1063-6919 |
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| Abstract | Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the M eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more efficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningful performances on instance segmentation datasets. |
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| AbstractList | Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the M eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more efficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningful performances on instance segmentation datasets. |
| Author | Kim, Chang-Su Park, Wonhui Jin, Dongkwon |
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| Snippet | Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing... |
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| SubjectTerms | Approximation algorithms categorization Computer vision grouping and shape analysis; Low-level vision; Recognition: detection Matrix decomposition Pattern recognition retrieval Segmentation Training |
| Title | Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation |
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