Fast Segmentation via Randomized Hashing
Camillo Jose Taylor, Anthony Cowley · 2009
This paper describes a feature based approach to segmenting images into coherent regions. The method draws inspiration from earlier work on randomized projection schemes for approximate nearest neighbor computation. The method proceeds by first computing a descriptor vector for each of the pixels in the image. These vectors are then randomly hashed to yield binary vectors. Salient clusters in the hash space are automatically identified by considering the populations associated with various hash codes. Since the method avoids the explicit vector distance computations associated with other methods, it is very amenable to fast implementation. Experimental results are presented on standard data sets. 1 Introduction and Related Work Segmentation, the problem of breaking an image into coherent regions is, of course, a fundamental problem in Computer Vision. This paper proposes a new approach to the segmentation problem that leverages ideas developed in the Theoretical Computer Science literature to derive a new feature space based clustering algorithm that is amenable to real time implementation.