Histopathology Image Categorization with Discriminative Dimension Reduction of Fisher Vectors

Abstract

In this paper, we present a histopathology image categorization method based on Fisher vector descriptors. While Fisher vector has been broadly successful for general computer vision and recently applied to microscopy image analysis, its feature dimension is very high and this could affect the classification performance especially when there is small amount of training images available. To address this issue, we design a dimension reduction algorithm in a discriminative learning model with similarity and representation constraints. In addition, to obtain the image-level Fisher vectors, we incorporate two types of local descriptors based on the standard texture feature and unsupervised feature learning. We use three publicly available datasets for experiments. Our evaluation shows that our overall approach achieves consistent performance improvement over existing approaches, our proposed discriminative dimension reduction algorithm outperforms the common dimension reduction techniques, and different local descriptors have varying effects on different datasets.