Who and Where: People and Location Co-Clustering
This addresses image clustering for applications like photo organization, but appears incremental as it builds on existing co-clustering methods.
The paper tackles the problem of clustering images containing both people and location patches by exploiting correlations between these domains, proposing a semi-supervised co-clustering algorithm that updates correlation links at runtime and simultaneously clusters both domains. Results show this correlation improves clustering performance, though no concrete numbers are provided.
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.