A Markov Chain based Ensemble Method for Crowdsourced Clustering
This work addresses the challenge of efficiently managing image clustering tasks by leveraging crowd workers, though it appears incremental in its approach.
The paper tackles the problem of combining multiple clustering solutions from crowdsourced image annotations by introducing a Markov chain based ensemble method to achieve consensus, resulting in improved clustering quality.
In presence of multiple clustering solutions for the same dataset, a clustering ensemble approach aims to yield a single clustering of the dataset by achieving a consensus among the input clustering solutions. The goal of this consensus is to improve the quality of clustering. It has been seen that there are some image clustering tasks that cannot be easily solved by computer. But if these images can be outsourced to the general people (crowd workers) to group them based on some similar features, and opinions are collected from them, then this task can be managed in an efficient manner and time effective way. In this work, the power of crowd has been used to annotate the images so that multiple clustering solutions can be obtained from them and thereafter a Markov chain based ensemble method is introduced to make a consensus of multiple clustering solutions.