Points of interest extraction using pairwise clustering and spatial features

In this work, the idea of local features extraction from image data based on points of interest, is revised. The method is based on a nonparametric pairwise clustering algorithm and the application of Hubert's test statistic. The clustering algorithm iteratively partitions the input image data until it finally converges to 2 classes. On the other hand the use of Hubert's test guarantees that the 2 classes in the feature space are associated with a well organized structure in the image plane. Both algorithms utilize the dissimilarity matrix of the input data. The validity of the approach is demonstrated by applying the method to an image retrieval system.