Adaptive Mean Shift Based Clustering in High Dimensions

Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. We exploit a recently proposed approximation technique, locality-sensitive hashing (LSH), to reduce the computational complexity of adaptive mean shift. In our implementation of LSH the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. The algorithm is tested on two applications. In the first, the performance of mode and k-means-based textons are compared in a texture classification study. In the second, multispectral images are segmented. Again, our method is compared to k-means clustering.