| Online-Ressource |
Verfasst von: | Kiechle, Martin [VerfasserIn]  |
| Storath, Martin [VerfasserIn]  |
| Weinmann, Andreas [VerfasserIn]  |
| Kleinsteuber, Martin [VerfasserIn]  |
Titel: | Model-based learning of local image features for unsupervised texture segmentation |
Verf.angabe: | Martin Kiechle, Martin Storath, Andreas Weinmann, and Martin Kleinsteuber |
E-Jahr: | 2018 |
Jahr: | January 26, 2018 |
Umfang: | 14 S. |
Fussnoten: | Gesehen am 21.10.2019 |
Titel Quelle: | Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on image processing |
Ort Quelle: | New York, NY : IEEE, 1992 |
Jahr Quelle: | 2018 |
Band/Heft Quelle: | 27(2018), 4, Seite 1994-2007 |
ISSN Quelle: | 1941-0042 |
Abstract: | Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this paper, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images. |
DOI: | doi:10.1109/TIP.2018.2792904 |
URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Resolving-System: https://doi.org/10.1109/TIP.2018.2792904 |
| Volltext: https://ieeexplore.ieee.org/document/8255629 |
| DOI: https://doi.org/10.1109/TIP.2018.2792904 |
Datenträger: | Online-Ressource |
Sprache: | eng |
Sach-SW: | Computational modeling |
| Cost function |
| Data models |
| feature extraction |
| feature vector |
| geometric optimization |
| ground truth segmentation |
| histological images |
| image patches |
| image segmentation |
| Image segmentation |
| image texture |
| learning (artificial intelligence) |
| learning process |
| local image features |
| Mathematical model |
| model-based learning |
| Mumford-Shah model |
| piecewise constant feature image |
| piecewise constant Mumford-Shah model |
| Prague texture segmentation benchmark |
| suitable convolutional features |
| textural patterns |
| Texture segmentation |
| texture segmentation methods |
| Training data |
| unsupervised learning |
| unsupervised texture segmentation |
K10plus-PPN: | 1679247190 |
Verknüpfungen: | → Zeitschrift |
Model-based learning of local image features for unsupervised texture segmentation / Kiechle, Martin [VerfasserIn]; January 26, 2018 (Online-Ressource)