Method for extracting textural features of robust

Abstract

The invention discloses a method for extracting textural features of a robust, and belongs to the technical field of image processing. The method comprises the implementation steps: pre-processing an input image, generating a feature set F, carrying out binaryzation on the feature set F based on a threshold value of each feature, carrying out binary encoding and generating a particular pixel label, carrying out rotation invariant even local binary pattern (LBP) encoding on the input image, generating an LBP label of each pixel point, constructing a 2-D coexistence histogram by the particular pixel label and the LBP label of each pixel point, vectoring the coexistence histogram and then applying the coexistence histogram into textural expression. The method is applied so as to reduce binary quantitative loss in an existing LBP mode, at the same time maintain robustness of changes of illumination, rotation, scale and visual angles by the extraction features.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a robust texture feature extraction method. Background technique [0002] Texture features play an important role in visual recognition, and have been widely studied and applied in the fields of texture classification, retrieval, synthesis and segmentation. In general, texture images not only exhibit a wide variety of geometric and illumination variations, but are often accompanied by drastic intra- and inter-class variations. Texture classification is a difficult task when prior knowledge is not available. Therefore, extracting robust texture features is at the core of solving these tasks. [0003] In the past few decades, many methods for extracting texture features have been proposed. Early research mainly focused on different statistical-based methods, model-based and signal processing features, such as co-occurrence matrices, Markov random fields and ...

AI Technical Summary

Problems solved by technology

[0006] LBP exchanges the efficiency of extracting local structure information at the cost of binary quantization, and its robustness to noise is poor. In order to effectively reduce its quantization loss and maintain the robustness of extracted features, it is necessary to improve the current LBP method.

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