#284:
Z. A. Oraibi,
H. Yousif,
A. Hafiane,
G. Seetharaman, and
K. Palaniappan
25th IEEE International Conference on Image Processing (ICIP),
pgs. 2446-2450,
2018
Abstract,
Bibtex,
PlainText,
PDF,
URL,
DOI,
Google Scholar
Automatic image classification systems for indirect immunofluorescence (IIF) labeling of human epithelial (HEp-2) cell specimens are needed to improve the efficient management of autoimmune diseases. In this paper, we propose to classify HEp-2 cell specimen imagery using a combination of local features and deep learning features extracted from the IIF images. Two local descriptors are used to capture texture information, namely: Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) extending the LBP descriptor and Joint Motif Labels (JML) based on the Peano scan motif concept. Deep learning features are then extracted using the VGG-19 image classification network. Finally, all descriptors are combined using a late fusion approach with a Random Forests (RF) classifier with seven output classes. Experimental results show that our proposed framework achieves a mean class accuracy of 92.11% with five-fold cross validation using the RF classifier with 1000 trees on the HEp-2 specimen benchmark dataset, which outperforms the state-of-the-art accuracy on this dataset.
@inproceedings{2018a,
author = "Z. A. Oraibi and H. Yousif and A. Hafiane and G. Seetharaman and K. Palaniappan",
title = "Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests",
year = 2018,
journal = "25th IEEE International Conference on Image Processing (ICIP)",
publisher = "IEEE",
pages = "2446-2450",
month = "Sep",
keywords = "feature extraction, random forests, local features, deep learning, image classification",
doi = "10.1109/ICIP.2018.8451287",
url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Learning+Local+and+Deep+Features+for+Efficient+Cell+Image+Classification+Using+Random+Forests&btnG="
}
Z. A. Oraibi, H. Yousif, A. Hafiane, G. Seetharaman, and K. Palaniappan. Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests. 25th IEEE International Conference on Image Processing (ICIP), IEEE, pages 2446-2450, September 2018.