Clickstream analysis for crowd-based object segmentation with confidence

TL;DR

This paper introduces a novel method that uses clickstream data to assess the quality of crowd-sourced image segmentations, improving accuracy and merging efficiency in large-scale annotation tasks.

Contribution

It presents a new approach to estimate segmentation quality from clickstream data, enhancing crowd annotation reliability without class-specific training.

Findings

High accuracy in quality estimation from clickstream data

Outperforms existing methods for merging annotations

Applicable across different object classes without retraining

Abstract

With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation; however, quality control remains a major issue which needs to be addressed. To our knowledge, we are the first to analyze the annotation process to improve crowd-sourced image segmentation. Our method involves training a regressor to estimate the quality of a segmentation from the annotator's clickstream data. The quality estimation can be used to identify spam and weight individual annotations by their (estimated) quality when merging multiple segmentations of one image. Using a total of 29,000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations. As the regressor does not need to be trained on the object class that it is applied to it can be regarded as a low-cost option for quality control and confidence analysis in the context of crowd-based image annotation.