The invention discloses a multi-resolution LBP (short for MR-LBR)
textural feature extracting method, and belongs to the technical field of image
information processing. The method includes the steps that firstly, an input image is preprocessed; secondly, image signals are decomposed and expressed through first-level discrete
wavelet transformation, and a high-frequency mean chart is acquired; thirdly, rotation invariant unified LBP calculation is conducted on an original image, a low-frequency approximate subgraph and the high-frequency mean chart, and then an LBP
histogram of the original image, an LBP
histogram of the low-frequency similar subgraph and an LBP
histogram of the high-frequency mean chart are acquired; eventually, the three histograms are spliced into a multi-resolution LBP histogram in a non-overlapping mode for describing texture information of the image. By the application of the method, more
textural feature information of the image can be extracted, the defects of existing LBP in the
texture processing aspect are overcome, and robustness of extracted features on rotation, illumination and the like is maintained. The multi-resolution LBP
textural feature extracting method is applied to classification of fresh
green tea leaves, the classification effect is remarkable, and accuracy reaches up to over 92 %.