TY - GEN
T1 - Pairwise similarity for line extraction from distorted images
AU - Hino, Hideitsu
AU - Fujiki, Jun
AU - Akaho, Shotaro
AU - Mochizuki, Yoshihiko
AU - Murata, Noboru
PY - 2013
Y1 - 2013
N2 - Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.
AB - Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.
KW - distorted image
KW - line detection
KW - pairwise similarity
KW - spectral clustering
UR - http://www.scopus.com/inward/record.url?scp=84884497622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884497622&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40246-3_31
DO - 10.1007/978-3-642-40246-3_31
M3 - Conference contribution
AN - SCOPUS:84884497622
SN - 9783642402456
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 250
EP - 257
BT - Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
T2 - 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
Y2 - 27 August 2013 through 29 August 2013
ER -