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Pregled bibliografske jedinice broj: 886181

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


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.).
Orlando, Florida: SPIE, 2017. str. 1014003-1 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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

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

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the SPIE / Gurcan, Metin N ; Tomaszewski, John E. - : SPIE, 2017, 1014003-1

Skup
Medical Imaging Symposium 2017 - Digital Pathology Conference

Mjesto i datum
Orlando, Florida, 11-16.02.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Maria Brbić (autor)

Avatar Url Ivica Kopriva (autor)

Avatar Url Dijana Tolić (autor)

Avatar Url Nino Antulov-Fantulin (autor)

Citiraj ovu publikaciju:

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.).
Orlando, Florida: SPIE, 2017. str. 1014003-1 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Kopriva, I., Brbić, M., Tolić, D., Antulov-Fantulin, N. & Chen, X. (2017) Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens. U: Gurcan, M. & Tomaszewski, J. (ur.)Proceedings of the SPIE.
@article{article, year = {2017}, pages = {1014003-1-1014003-10}, keywords = {subspace clustering, one-dimensional subspace, reproducible kernel Hilbert space, image segmentation}, title = {Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens}, keyword = {subspace clustering, one-dimensional subspace, reproducible kernel Hilbert space, image segmentation}, publisher = {SPIE}, publisherplace = {Orlando, Florida} }
@article{article, year = {2017}, pages = {1014003-1-1014003-10}, keywords = {subspace clustering, one-dimensional subspace, reproducible kernel Hilbert space, image segmentation}, title = {Fast clustering in linear 1D subspaces: segmentation of microscopic image of unstained specimens}, keyword = {subspace clustering, one-dimensional subspace, reproducible kernel Hilbert space, image segmentation}, publisher = {SPIE}, publisherplace = {Orlando, Florida} }




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