1 May 1992 Adaptive machine learning algorithm for multispectral image analysis
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This paper describes a machine learning algorithm for analyzing the multispectral images of natural scenes. A mathematical training algorithm has been developed to guide the operation of a statistical pattern recognition technique for detecting and extracting the image clusters in a multidimensional feature space. For this purpose, the peak modality of l-D image histograms is selected as the mathematical training criterion. The algorithm is applied to the clusters of the color images of natural scenes in 3—D feature space. During the training process, image clusters are detected in some well—defined decision elements using constant lightness and chromaticity loci of the uniform color space. This gives non—parametric estimates of the clusters' distributions without imposing any constraints in their forms. The linear discriminant method is then used to project simultaneously the detected clusters onto a line for region isolation. This permits utilization of all the spectral properties for object recognition and inherently recognizes their respective cross correlation.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mehmet Celenk, Mehmet Celenk, } "Adaptive machine learning algorithm for multispectral image analysis", Proc. SPIE 1670, Color Hard Copy and Graphic Arts, (1 May 1992); doi: 10.1117/12.2322247; https://doi.org/10.1117/12.2322247

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