Investigation of Segmentation Based Pooling for Image Quantification

A key step in many image quantification solutions is feature pooling, where subsets of lower-level features are combined so that higher-level, more invariant predictions can be made. The pooling region which defines the subsets often has a fixed spatial size and geometry, but data adaptive pooling regions have also been used. In this paper we investigate pooling strategies for the data adaptive case and suggest a new framework for pooling that uses multiple sub-regions instead of a single region. We show that this framework can help represent the shape of the pooling region and also produce useful pairwise features for adjacent pooling regions. We demonstrate the utility of the framework in a number of classification tasks relevant to image quantification in digital microscopy.