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Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens (CROSBI ID 650340)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Kopriva, Ivica ; Brbić, Maria ; Tolić, Dijana ; Antulov-Fantulin, Nino ; Chen, Xinjian Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens // Proceedings of the SPIE / Gurcan, Metin N ; Tomaszewski, John E. (ur.). SPIE, 2017. str. 1014003-1-1014003-10

Podaci o odgovornosti

Kopriva, Ivica ; Brbić, Maria ; Tolić, Dijana ; Antulov-Fantulin, Nino ; Chen, Xinjian

engleski

Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens

Algorithms for subspace clustering (SC) are effective in terms of the accuracy but exhibit high computational complexity. We propose algorithm for SC of (highly) similar data points drawn from union of linear one- dimensional subspaces that are possibly dependent in the input data space. The algorithm finds a dictionary that represents data in reproducible kernel Hilbert space (RKHS). Afterwards, data are projected into RKHS by using empirical kernel map (EKM). Due to dimensionality expansion effect of the EKM one-dimensional subspaces become independent in RKHS. Segmentation into subspaces is realized by applying the max operator on projected data which yields the computational complexity of the algorithm that is linear in number of data points. We prove that for noise free data proposed approach yields exact clustering into subspaces. We also prove that EKM-based projection yields less correlated data points. Due to nonlinear projection, the proposed method can adopt to linearly nonseparable data points. We demonstrate accuracy and computational efficiency of the proposed algorithm on synthetic dataset as well as on segmentation of the image of unstained specimen in histopathology. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

subspace clustering ; one-dimensional subspace ; reproducible kernel Hilbert space ; image segmentation

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Podaci o prilogu

1014003-1-1014003-10.

2017.

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the SPIE

Gurcan, Metin N ; Tomaszewski, John E.

SPIE

Podaci o skupu

Medical Imaging Symposium 2017 - Digital Pathology Conference

predavanje

11.02.2017-16.02.2017

Orlando (FL), Sjedinjene Američke Države

Povezanost rada

Računarstvo