The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
Overlap and channel errors in adaptive vector quantization for image coding
Information Sciences—Informatics and Computer Science: An International Journal
An adaptive incremental LBG for vector quantization
Neural Networks
Engineering Applications of Artificial Intelligence
Fast VQ codebook search algorithm for grayscale image coding
Image and Vision Computing
Improved batch fuzzy learning vector quantization for image compression
Information Sciences: an International Journal
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
Clustering by competitive agglomeration
Pattern Recognition
Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation
Engineering Applications of Artificial Intelligence
Fast on-line signature recognition based on VQ with time modeling
Engineering Applications of Artificial Intelligence
An efficient prediction algorithm for image vector quantization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Fuzzy vector quantization algorithms and their application in image compression
IEEE Transactions on Image Processing
An axiomatic approach to soft learning vector quantization and clustering
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
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The implementation of fuzzy clustering in the design process of vector quantizers faces three challenges. The first is the high computational cost. The second challenge arises because a vector quantizer is required to assign each training sample to only one cluster. However, such an aggressive interpretation of fuzzy clustering results to a crisp partition of inferior quality. The third one is the dependence on initialization. In this paper we develop a fuzzy clustering-based vector quantization algorithm that deals with the aforementioned problems. The algorithm utilizes a specialized objective function, which involves the c-means and the fuzzy c-means along with a competitive agglomeration term. The joint effect is a learning process where the number of codewords (i.e. cluster centers) affected by a specific training sample is gradually reducing and therefore, the number of distance calculations is also reducing. Thus, the computational cost becomes smaller. In addition, the partition is smoothly transferred from fuzzy to crisp conditions and there is no need to employ any aggressive interpretation of fuzzy clustering. The competitive agglomeration term refines large clusters from small and spurious ones. Then, contrary to the classical competitive agglomeration method, we do not discard the small clusters but instead migrate them close to large clusters, rendering more competitive. Thus, the codeword migration process uses the net effect of the competitive agglomeration and acts to further reduce the dependence on initialization in order to obtain a better local minimum. The algorithm is applied to grayscale image compression. The main simulation findings can be summarized as follows: (a) a comparison between the proposed method and other related approaches shows its statistically significant superiority, (b) the algorithm is a fast process, (c) the algorithm is insensitive with respect to its design parameters, and (d) the reconstructed images maintain high quality, which is quantified in terms of the distortion measure.