Surface grading of bamboo strips using multi-scale color texture features in eigenspace

In order to achieve high competitive quality of bamboo products, it appears that bamboo strips with naturally different tonalities should be elaborately sorted into different classes according to their global color texture appearance. Inspired by the coarse-to-fine visual perception process of human vision system, this paper proposes a new surface grading approach by integrating the color and texture of bamboo strips based on Gaussian multi-scale space. The multi-scale representations of color texture for the original image of bamboo strips could be obtained and used to construct the multivariate image, each channel of which represents a perceptual observation from different scales. The multivariate image analysis (MIA) techniques are used to extract multi-scale features from the resulting multivariate image data. The characteristic images corresponding to typical classes are selected to build the model of the reference eigenspace. The novel testing images and the training images are all projected onto this reference eigenspace to obtain their representative feature clusters. And the Bhattacharyya distance is used to estimate the similarity of the representative feature clusters between the testing images and the training images in the eigenspace. Then a k-NN classifier is adopted to classify the testing images into the given classes of training images. Comparative experiments have been carried out on a set of actual bamboo strip images and the experimental results verify the effective discrimination of multi-scale color texture eigenspace features and good classification accuracy of the proposed surface grading method.