Learning to segment on tiny datasets: a new shape model

TL;DR

This paper introduces a novel shape-based segmentation method that effectively works with tiny datasets, combining shape descriptors, bag-of-words detection, and dense CRF optimization to achieve near state-of-the-art results.

Contribution

A new automatic part-based shape model for object segmentation that performs well with minimal training data.

Findings

Achieves near state-of-the-art results with tiny datasets.

Uses a novel shape descriptor for local boundary modeling.

Combines shape detection with dense CRF for segmentation.

Abstract

Current object segmentation algorithms are based on the hypothesis that one has access to a very large amount of data. In this paper, we aim to segment objects using only tiny datasets. To this extent, we propose a new automatic part-based object segmentation algorithm for non-deformable and semi-deformable objects in natural backgrounds. We have developed a novel shape descriptor which models the local boundaries of an object's part. This shape descriptor is used in a bag-of-words approach for object detection. Once the detection process is performed, we use the background and foreground likelihood given by our trained shape model, and the information from the image content, to define a dense CRF model. We use a mean field approximation to solve it and thus segment the object of interest. Performance evaluated on different datasets shows that our approach can sometimes achieve results near state-of-the-art techniques based on big data while requiring only a tiny training set.