Hierarchical clustering of EMD based interest points for road sign detection

• Automatic road sign detection and recognition system based on interest points and JTC.
• Interest point detection from the image edges based on the 1-D empirical mode decomposition (EMD).
• Two-step unsupervised hierarchical clustering technique to cluster selected interest points.
• Clustering is based on two criteria and adaptive, which yields the regions of interest (ROIs).
• Application of FJTC as a classification tool, to match the unknown signs with the reference signs.

This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.