Title :
Improved Mean Shift Spectral Clustering Based on Reduced Set Density Estimator
Author :
Qian, Pengjiang ; Wang, Shitong ; Wu, Xiaojun ; Deng, Zhaohong ; Sang, Qingbing
Author_Institution :
Sch. of Inf. Technol., Jiangnan Univ., Wuxi, China
Abstract :
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator (RSDE). Due to just a few sample points in the reduced set being referred to, the time complexity of mean shift embedded in IMSSC decreases to O(mN) and the total computational costs of IMSSC are sharply reduced.
Keywords :
computational complexity; estimation theory; image segmentation; pattern clustering; image segmentation; improved mean shift spectral clustering algorithm; reduced set density estimator; time complexity; Clustering algorithms; Computational efficiency; Constraint optimization; Convergence; Image segmentation; Information technology; Kernel; Minimization methods; Probability density function; Testing;
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
DOI :
10.1109/CCPR.2009.5343985
