Sparse non-parametric Bayesian model for HEP-2 cell image classification

This paper studies automated classification of Human Epithelial Type-2 (HEp-2) cell images which is essential in diagnosing the Autoimmune Diseases (AD). The prevalent approach for this problem makes use of the Bag of Words (BoW) model and sparse coding scheme on over complete dictionaries, where the dictionary dimension is usually much larger than feature dimension. In addition, this approach usually requires manual selection of the dictionary dimension which is often troublesome and dependent highly on specific applications and datasets. We proposed a non-parametric Bayesian model that is capable of determining the dictionary dimension automatically by exploiting the Indian Buffet Process (IBP). This proposed model has been evaluated on two public HEp-2 benchmarking datasets, i.e., ICPR2012 and ICIP2013 where the SIFT and SURF features of the cell image are extracted in a grid manner and used as the input. Experiments show that the proposed model obtained state-of-the-art cell classification accuracy. More importantly, the dictionary dimension learned by the proposed model is around 29 and 8 times lower than the over complete dictionaries, respectively, for two benchmarking datasets. This low-dimensional dictionary helps to reduce the computational cost significantly for the cell classification task.