@inproceedings{f2497f84e0284a75aeab7389b5429c5b,
title = "Bone texture characterization with fisher encoding of local descriptors",
abstract = "Bone texture characterization is important for differentiating osteoporotic and healthy subjects. Automated classification is however very challenging due to the high degree of visual similarity between the two types of images. In this paper, we propose to describe the bone textures by extracting dense sets of local descriptors and encoding them with the improved Fisher vector (IFV). Compared to the standard bag-of-visual-words (BoW) model, Fisher encoding is more discriminative by representing the distribution of local descriptors in addition to the occurrence frequencies. Our method is evaluated on the ISBI 2014 challenge dataset of bone texture characterization, and we demonstrate excellent classification performance compared to the challenge entries and large improvement over the BoW model.",
keywords = "Bone texture, Fisher vector, classification, feature encoding",
author = "Yang Song and Weidong Cai and Fan Zhang and Heng Huang and Yun Zhou and \{Dagan Feng\}, David",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7163803",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "5--8",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
}