@inproceedings{045def8b78464e5da535e5e4f3420435,
title = "Comparison of k-means related clustering methods for nuclear medicine images segmentation",
abstract = "In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.",
keywords = "Image segmentation, PET, k-means, nuclear medicine",
author = "Damian Borys and Pawel Bzowski and Marta Danch-Wierzchowska and Krzysztof Psiuk-Maksymowicz",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 9th International Conference on Machine Vision, ICMV 2016 ; Conference date: 18-11-2016 Through 20-11-2016",
year = "2017",
doi = "10.1117/12.2268825",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Nikolaev, \{Dmitry P.\} and Antanas Verikas and Jianhong Zhou and Petia Radeva and Wei Zhang",
booktitle = "Ninth International Conference on Machine Vision, ICMV 2016",
address = "United States",
}