Competitive learning based on kernel functions and quadtree for image segmentation

Tao Guan; Ling-Ling Li; Yan-Jun Zhang

doi:10.1117/12.913379

30 September 2011 Competitive learning based on kernel functions and quadtree for image segmentation

Tao Guan, Ling-Ling Li, Yan-Jun Zhang

Author Affiliations +

Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82850Y (2011) https://doi.org/10.1117/12.913379
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt

Abstract

This paper proposes an online competitive learning algorithm, briefly denoted as KACL (Kernel Averaging Competitive Learning), using kernel functions and quadtree structure for clustering analysis and remotely sensed image segmentation. Initially, KACL constructs a quadtree with a pre-specified scale from online input data and then locates all clusters by moving or self-splitting the nodes of the quadtree. The complexity of the quadtree is decided by the distribution of data. In the learning rule of KACL, we use the local means of vectors but not single vector so as to avoid the movement of learning prototypes among different clusters. We give the mathematical properties of KACL and present the proof of its convergence. KACL avoids the dead node problem and presetting of the number of clusters. Two experiments are separately carried out on Gaussian mixture data sets and remote sensing images and the results have shown good performance.

Citation Download Citation

Tao Guan, Ling-Ling Li, and Yan-Jun Zhang "Competitive learning based on kernel functions and quadtree for image segmentation", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82850Y (30 September 2011); https://doi.org/10.1117/12.913379

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