Method, apparatus, and computer program product for image clustering

An apparatus comprising:

• at least one processor;
• and
• at least one memory comprising computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to:
• receive a plurality of input clusters including a first cluster and at least a second cluster, input clusters including at least one feature, wherein features are extracted from an image, and clusters having a medoid, the medoid of a given cluster being a particular feature of the given cluster that, relative to distances between the features within the given cluster, is closest to the most distant feature from that particular feature;
• determine a first medoid for the first cluster and a second medoid for the second cluster;
• determine an inter-cluster distance between the first medoid and the second medoid;
• merge the first cluster with the second cluster to generate a merged cluster in an instance in which the inter-cluster distance is less than a distance threshold;
• receive a plurality of features derived from a plurality of facial images;
• split the plurality of features into two clusters using a clustering algorithm in an instance in which a medoid of the plurality of features is farther than the threshold distance from any feature within plurality of features;
• recursively split clusters in two additional clusters using the clustering algorithm in an instance in which a medoid of the respective cluster is farther than the threshold distance from any feature within the respective cluster;
• and
• include the clusters resulting from the recursive splitting in the plurality of input clusters.

A computer program product comprising at least one computer readable medium having program code stored thereon, the program code which when executed by an apparatus cause the apparatus at least to:

• receive a plurality of input clusters including a first cluster and at least a second cluster, input clusters including at least one feature, wherein features are extracted from an image, and clusters having a medoid, the medoid of a given cluster being a particular feature of the given cluster that, relative to distances between the features within the given cluster, is closest to the most distant feature from that particular feature;
• determine a first medoid for the first cluster and a second medoid for the second cluster;
• determine an inter-cluster distance between the first medoid and the second medoid;
• merge the first cluster with the second cluster to generate a merged cluster in an instance in which the inter-cluster distance is less than a distance threshold;
• receive a plurality of features derived from a plurality of facial images;
• split the plurality of features into two clusters using a clustering algorithm in an instance in which a medoid of the plurality of features is farther than the threshold distance from any feature within plurality of features;
• recursively split clusters in two additional clusters using the clustering algorithm in an instance in which a medoid of the respective cluster is farther than the threshold distance from any feature within the respective cluster;
• and
• include the clusters resulting from the recursive splitting in the plurality of input clusters.