Adaptive quantization of color space for recognition of finished wooden components

The paper concerns the recognition of textured objects, such as stained wooden parts, using color images. Many existing color classification systems utilize histogram-based similarity measures to compare an observed image with models from a database. Although the performance of these systems depends heavily on proper quantization of the color space, most quantization methods are based on traditional clustering or thresholding operations. The authors describe a novel approach to color space quantization in which the intersection of meaningful representations results in a partition of the color space. The color descriptions are chosen adaptively, using a set of training images. The resulting partition serves as the domain for histograms of models and of observed images and information-theoretic similarity measures are used to perform recognition. The motivation for this system is to achieve high recognition accuracy in an industrial setting. Laboratory tests have demonstrated a high level of accuracy for this technique, even though the objects of interest exhibit large variations of texture and color.