Who and Where: People and Location Co-Clustering

TL;DR

This paper introduces a semi-supervised co-clustering algorithm that leverages the correlation between people and location domains in images, improving clustering accuracy by updating links dynamically.

Contribution

It presents a novel semi-supervised co-clustering method that adaptively updates domain correlations during clustering, enhancing image clustering performance.

Findings

Correlation-aware clustering improves accuracy

Dynamic link updating benefits clustering quality

Effective on real-world datasets

Abstract

In this paper, we consider the clustering problem on images where each image contains patches in people and location domains. We exploit the correlation between people and location domains, and proposed a semi-supervised co-clustering algorithm to cluster images. Our algorithm updates the correlation links at the runtime, and produces clustering in both domains simultaneously. We conduct experiments in a manually collected dataset and a Flickr dataset. The result shows that the such correlation improves the clustering performance.