Vector quantization clustering using lattice growing search

In this paper we introduce a non-iterative algorithm for vector quantization clustering based on the efficient search for the two clusters whose merging gives the minimum distortion increase. The search is performed within the K-dimensiona1 cells of a lattice having a generating matrix that changes from one step of the algorithm to another. The generating matrix is modified gradually so that the lattice cells grow in volume, allowing the search of the two closest clusters in an enlarged neighborhood. We call this algorithm Lattice Growing Search (LGS) clustering. Preliminary results on 512 x 512 images encoded at 0.5 bits/pixel showed that the LGS technique can produce codebooks of similar quality in less than 1/10 of the time required by the LBG algorithm [9].