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A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification

Abstract : In this paper, we propose a new approach for color image simplification in order to improve flame pixel classification. The fire detection performance depends critically on the performance of the flame pixel classifier. Color image simplification is the process of simplifying an image in order to decrease the number of colors while preserving, as much as possible, shapes. In this work, a hierarchical clustering method in a given color space is used to map the original colors into a smaller set of representative ones, allowing the use of a simple heuristic rule for classifying the clusters related to candidate flame colors. Using reverse mapping, we identify possible flame colors in the image. Main contributions of our work are the application of a simple hierarchical clustering method to color simplification, that decreases the number of possible flame colors, and a filtering methodology to reduce the influence of outliers. Several color spaces and distance measures were used to evaluate the proposed method. Experimental results demonstrate that color simplification is essential to successfully employ heuristic classification of flame colors.
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Contributor : Jean Cousty <>
Submitted on : Monday, September 10, 2012 - 1:54:26 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM


  • HAL Id : hal-00730479, version 1


Kleber Souza, Silvio Guimarães, Zenilton Patrocino Jr., Arnaldo de Albuquerque Araujo, Jean Cousty. A Simple Hierarchical Clustering Method for Improving Flame Pixel Classification. 23rd International Conference on Tools with Artificial Intelligence (ICTAI'2011), Nov 2011, United States. pp.110-117. ⟨hal-00730479⟩



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