Agglomerative clustering for feature point grouping

The objective of this paper is to group feature points on different planes as a means of semantic image segmentation and understanding. The methodology is based on the ability to estimate planar homographies from grouped feature points spanning different unknown number of planes. This paper proposes an alternative to the J-linkage method, which was shown to have benefits in terms of accuracy over other multiple model estimation techniques. J-linkage is an agglomerative clustering technique that uses a set representation of support for a set of possible planar homographies and the Jaccard measure to determine the distance between support sets. The technique proposed in this paper uses a frequency vector to represent the support for a model. This formulation promotes clustering even in the presence of noise and prevents the order in which agglomerative clustering is performed from influencing the results. The feature vector representation requires an alternative distance measure to Jaccard to be exercised, that of cosine similarity. Hence, the method proposed here is called C-linkage. The results show that, compared to the J-linkage method, the proposed technique correctly classifies more points on each plane, and results in less over-segmentation while providing higher Normalized Mutual Information scores for a range of multiple model estimation problems on different datasets.