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Supervised Evaluation of Image Segmentation and Object Proposal Techniques.

Jordi Pont-Tuset, Ferran Marques

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 29, 2015

Summary

This summary is machine-generated.

This study introduces a new method for evaluating image segmentation and object proposal algorithms. Precision-recall curves for boundaries and objects-and-parts are proposed as the best tools for supervised evaluation.

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Area of Science:

Computer Vision
Machine Learning
Image Analysis

Background:

Supervised evaluation of image segmentation and object proposal algorithms is crucial for advancing computer vision.
Existing evaluation measures are numerous, sometimes redundant, and their comparative quality is not well-established.

Purpose of the Study:

To survey, structure, and deduplicate existing evaluation measures for image segmentation and object proposals.
To propose a novel evaluation measure: precision-recall for objects and parts.
To quantitatively compare the quality of different evaluation measures using meta-measures.

Main Methods:

A comprehensive survey and deduplication of existing evaluation metrics.
Development of a new precision-recall measure for objects and parts.
Analysis of eight state-of-the-art object proposal techniques.
Presentation of two quantitative meta-measures to assess metric performance on nine segmentation methods.

Main Results:

Identified and structured a comprehensive set of evaluation measures.
Proposed and validated the precision-recall for objects and parts measure.
Demonstrated the effectiveness of meta-measures in comparing evaluation tools.
The tandem of precision-recall curves for boundaries and for objects-and-parts emerged as the preferred evaluation tool.

Conclusions:

The proposed precision-recall curves for boundaries and objects-and-parts offer a robust and comprehensive approach to supervised evaluation.
The developed meta-measures provide a framework for assessing the quality of evaluation metrics.
Publicly releasing datasets and code facilitates reproducible research in image segmentation evaluation.