Commonality Preserving Multiple Instance Clustering Based on Diverse Density

Image-set clustering is a problem decomposing a given image set into disjoint subsets satisfying specified criteria. For single vector image representations, proximity or similarity criterion is widely applied, i.e., proximal or similar images form a cluster. Recent trend of the image description, however, is the local feature based, i.e., an image is described by multiple local features, e.g., SIFT, SURF, and so on. In this description, which criterion should be employed for the clustering? As an answer to this question, this paper presents an image-set clustering method based on commonality, that is, images preserving strong commonality (coherent local features) form a cluster. In this criterion, image variations that do not affect common features are harmless. In the case of face images, hair-style changes and partial occlusions by glasses may not affect the cluster formation. We defined four commonality measures based on Diverse Density, that are used in agglomerative clustering. Through comparative experiments, we confirmed that two of our methods perform better than other methods examined in the experiments.