Hierarchical Semantic Segmentation using Psychometric Learning

TL;DR

This paper introduces a novel psychometric learning approach for semantic image segmentation that captures expert knowledge more effectively than traditional label-based methods, demonstrated on synthetic, aerial, and histology images.

Contribution

It develops a new psychometric testing-based annotation method combined with deep metric learning for detailed semantic segmentation.

Findings

Effective segmentation on synthetic images

Improved semantic understanding in aerial images

Enhanced histology image analysis

Abstract

Assigning meaning to parts of image data is the goal of semantic image segmentation. Machine learning methods, specifically supervised learning is commonly used in a variety of tasks formulated as semantic segmentation. One of the major challenges in the supervised learning approaches is expressing and collecting the rich knowledge that experts have with respect to the meaning present in the image data. Towards this, typically a fixed set of labels is specified and experts are tasked with annotating the pixels, patches or segments in the images with the given labels. In general, however, the set of classes does not fully capture the rich semantic information present in the images. For example, in medical imaging such as histology images, the different parts of cells could be grouped and sub-grouped based on the expertise of the pathologist. To achieve such a precise semantic representation of the concepts in the image, we need access to the full depth of knowledge of the annotator. In this work, we develop a novel approach to collect segmentation annotations from experts based on psychometric testing. Our method consists of the psychometric testing procedure, active query selection, query enhancement, and a deep metric learning model to achieve a patch-level image embedding that allows for semantic segmentation of images. We show the merits of our method with evaluation on the synthetically generated image, aerial image and histology image.