Z. A. Oraibi,
A. Hafiane, and
IEEE Applied Imagery Pattern Recognition Workshop (AIPR),
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.
author = "Z. A. Oraibi and M. Irio and A. Hafiane and K. Palaniappan",
title = "Texture Classification using Multiple Local Descriptors",
year = 2017,
journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
pages = "1-7"
Z. A. Oraibi, M. Irio, A. Hafiane, and K. Palaniappan. Texture Classification using Multiple Local Descriptors. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-7, 2017.