An adaptive weight texture classification method based on Local Binary Pattern Variance

The extraction of local texture information using the traditional Local Binary Mode (LBP) is limited, and it ignores the representation of global texture information, which leads to an unsatisfactory outcome for the texture classification task. Local Binary Mode (LBP) has been widely used in texture classification. This paper utilizes LBPV to resolve this issue (Local Binary Pattern Variance) and proposes a novel adaptive weight joint multi-scale LBPV2 texture picture classification algorithm. The typical variance weight is replaced by the square of covariance as the cumulative weight of the histogram in this method, and the multi-scale texture information is retrieved using an adaptive weight and multi-scale scheme. Thus, the texture classification performance is further improved. Simulation experiments on the commonly used Outex reference texture database show that the proposed adaptive weight combined with multi-scale LBPV2 can significantly improve the performance of texture classification. In the fields of computer vision and pattern recognition, texture analysis is a fundamental visual problem with a wide range of applications, including object detection, remote sensing, content-based image retrieval, and medical picture analysis. For various research questions, numerous academics have put forth various LBP versions in recent years. The dominant LBP [2] model was proposed by Liao et al. in 2009, and it was empirically chosen as the best model out of all the models. Guo et al. proposed LBPV [3], which expresses local contrast information into the straight square of texture images using a local variance confidence and global matching scheme. In order to increase classification performance, the author also proposed a Completed Local Binary Pattern (CLBP) [4] in the same year. This pattern combines three complimentary groups—CLBPS, CLBPM, and CLBPC—using a combined probability distribution. To enhance the traditional local binary pattern's noise resistance and texture expression, Liu et al. proposed the extension of LBP [5] in 2012. Relevant scholars developed a pixel block sampling structure and local neighborhood intensity relationship model for texture classification in 2013 on the basis of conventional LBP [6], and produced notable results. More recently, in 2017, a multiscale LBP [7] was presented, transcending the constraints of conventional LBP representation and not only reflecting the microscopic texture structure but also effectively expressing the macroscopic texture structure of bigger areas. Although LBP and its variations have produced a number of remarkable texture classification results, there are still a lot of possible shortcomings. The expression of nearly entirely lost global information, for instance, results in inadequate texture classification results because most LBP versions are only capable of representing local texture information [1]. In order to significantly improve texture performance, this paper proposed a new method of adaptive weight joint multi-scale LBPV2 for classifying texture images. It did this by introducing adaptive weight classification and by replacing the variance weight in the LBPV calculation method with the variance square as the cumulative weight of the histogram.