Abstract
In recent decades, the need for efficient and effective image search from large databases has increased. In this paper, we present a novel shape matching framework based on structures common to similar shapes. After representing shapes as medial axis graphs, in which nodes show skeleton points and edges connect nearby points, we determine the critical nodes connecting or representing a shape’s different parts. By using the shortest path distance from each skeleton (node) to each of the critical nodes, we effectively retrieve shapes similar to a given query through a transportation-based distance function. To improve the effectiveness of the proposed approach, we employ a unified framework that takes advantage of the feature representation of the proposed algorithm and the classification capability of a supervised machine learning algorithm. A set of shape retrieval experiments including a comparison with several well-known approaches demonstrate the proposed algorithm’s efficacy and perturbation experiments show its robustness.
| Original language | English |
|---|---|
| Pages (from-to) | 147-163 |
| Number of pages | 17 |
| Journal | Pattern Analysis and Applications |
| Volume | 22 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 5 2019 |
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Keywords
- Earth mover’s distance
- Medial axis graph
- Shape matching
- Shape retrieval
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Object recognition based on critical nodes. / Boluk, Arda; Demirci, M. Fatih.
In: Pattern Analysis and Applications, Vol. 22, No. 1, 05.02.2019, p. 147-163.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Object recognition based on critical nodes
AU - Boluk, Arda
AU - Demirci, M. Fatih
PY - 2019/2/5
Y1 - 2019/2/5
N2 - In recent decades, the need for efficient and effective image search from large databases has increased. In this paper, we present a novel shape matching framework based on structures common to similar shapes. After representing shapes as medial axis graphs, in which nodes show skeleton points and edges connect nearby points, we determine the critical nodes connecting or representing a shape’s different parts. By using the shortest path distance from each skeleton (node) to each of the critical nodes, we effectively retrieve shapes similar to a given query through a transportation-based distance function. To improve the effectiveness of the proposed approach, we employ a unified framework that takes advantage of the feature representation of the proposed algorithm and the classification capability of a supervised machine learning algorithm. A set of shape retrieval experiments including a comparison with several well-known approaches demonstrate the proposed algorithm’s efficacy and perturbation experiments show its robustness.
AB - In recent decades, the need for efficient and effective image search from large databases has increased. In this paper, we present a novel shape matching framework based on structures common to similar shapes. After representing shapes as medial axis graphs, in which nodes show skeleton points and edges connect nearby points, we determine the critical nodes connecting or representing a shape’s different parts. By using the shortest path distance from each skeleton (node) to each of the critical nodes, we effectively retrieve shapes similar to a given query through a transportation-based distance function. To improve the effectiveness of the proposed approach, we employ a unified framework that takes advantage of the feature representation of the proposed algorithm and the classification capability of a supervised machine learning algorithm. A set of shape retrieval experiments including a comparison with several well-known approaches demonstrate the proposed algorithm’s efficacy and perturbation experiments show its robustness.
KW - Earth mover’s distance
KW - Medial axis graph
KW - Shape matching
KW - Shape retrieval
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UR - http://www.scopus.com/inward/citedby.url?scp=85059856037&partnerID=8YFLogxK
U2 - 10.1007/s10044-018-00777-w
DO - 10.1007/s10044-018-00777-w
M3 - Article
AN - SCOPUS:85059856037
VL - 22
SP - 147
EP - 163
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
SN - 1433-7541
IS - 1
ER -