基于局部特征多轴旋转不变特性的纹理图像分类算法

doi:10.11896／j.issn.1002-137X.2018.12.034

Abstract

Abstract: This paper adopted a non-quantifiable local feature to design a robust texture descriptor,so as to enhance the robustness of the texture classification in the rotation and scale changes.First of all,the concept of local feature with rotational symmetry is introduced.It is found that many rotation invariant local features are rotational symmetric to a certain degree.Therefore,this paper proposed a novel local feature to describe the rotation invariant properties of the texture.In order to deal with the change of rotation and scale in texture image,Fisher vector encoding method is used to manage multiscale analysis for the texture feature,which can combine with the scale information without increasing the dimension of the local feature.The resulting local features have strong robustness to rotation and gray intensity variation.Experimental results show that the proposed method outperforms the existing algorithms on many data sets,greatly improving the texture classification accuracy.

Key words: Fisher vector, Local feature, Rotation invariant, Scale change, Texture descriptor

CLC Number:

TP391

HUANG Qing-yu, ZHANG Deng-yi. Classification Algorithm for Texture Image Based on Local Characteristics of N-FoldRotation Invariant Feature[J].Computer Science, 2018, 45(12): 206-209.

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