Unsupervised color image segmentation using a lattice algebra clustering technique

Gonzalo Urcid; Gerhard X. Ritter

doi:10.1117/12.902214

2 November 2011 Unsupervised color image segmentation using a lattice algebra clustering technique

Gonzalo Urcid, Gerhard X. Ritter

Proceedings Volume 8011, 22nd Congress of the International Commission for Optics: Light for the Development of the World; 80117D (2011) https://doi.org/10.1117/12.902214
Event: International Commission for Optics (ICO 22), 2011, Puebla, Mexico

Abstract

In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

Citation Download Citation

Gonzalo Urcid and Gerhard X. Ritter "Unsupervised color image segmentation using a lattice algebra clustering technique", Proc. SPIE 8011, 22nd Congress of the International Commission for Optics: Light for the Development of the World, 80117D (2 November 2011); https://doi.org/10.1117/12.902214

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KEYWORDS

Image segmentation

RGB color model

Matrices

Algorithm development

Color image segmentation

Image processing

Image processing algorithms and systems

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