ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-time K-Means Clustering for Color Images on Reconfigurable Hardware
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
K-means Clustering for Multispectral Images Using Floating-Point Divide
FCCM '07 Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
An on-chip-trainable Gaussian-Kernel analog support vector machine
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Bandwidth adaptive hardware architecture of K-Means clustering for video analysis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
GPU-Based Multilevel Clustering
IEEE Transactions on Visualization and Computer Graphics
A Conscience On-line Learning Approach for Kernel-Based Clustering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Hardware-driven adaptive k-means clustering for real-time video imaging
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Flexible Hardware Architecture of Hierarchical K-Means Clustering for Large Cluster Number
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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A hardware-efficient on-line-learnable processor was developed for the K-means clustering of highly dimensional vectors. Based on our proposed sample updating strategy, an incremental number of sample vectors can be clustered by a constant set of VLSI circuits. In order to speed up the learning process, we developed an analog fully parallel self-converging circuitry to implement the K-means algorithm. Upon receiving a sample vector on-line, the K-means learning autonomously proceeds and converges within a single system clock cycle (0.1 μs at 10 MHz). Furthermore, the chip-area and inner connection explosion problem was solved by using the proposed architecture. A proof-of-concept processor was designed and verified by the HSPICE and Nanosim simulations. The images from an actual database were used as learning samples in the form of 64 dimensional feature vectors. From the simulation results, all the samples were clustered into correct categories with a randomly ill initialization. In addition, the number of sample vectors can be freely increased.