Effective place scene clustering using straight lines

Hyewon Moon, Jin Han Lee, Sehyung Lee, Il Hong Suh

Abstract: In this paper, we propose a scene clustering algorithm which uses straight line features. Scenes are represented as nodes in the graph, and each connectivity between nodes is calculated by a pre-trained vocabulary tree. By applying a spectral clustering algorithm to the constructed graph, the scenes are partitioned into k groups where k is determined by the proposed estimation method. Instead of using the standard eigenvalue analysis, the optimal k is computed so that the partitioned graph becomes to have strong intra-class correlations while inter-class correlations are relatively weak. As a result, scenes are adaptively clustered according to the environmental changes. The clustering performance of the proposed method is quantitatively evaluated with three image sequences captured in challenging environments. Experimental comparisons demonstrate that our line-based algorithm outperforms existing algorithms utilizing other types of features as well as the produced scene clustering results are more human-like.