Adaptive Dictionary Generation Model based Texture Recognition
Pengwen Xiong, Yuexi Wang, Weiping Zhang, Changcheng Wu · 2024
While traditional dictionary learning models under one-shot learning always have the problem of low accuracy of texture recognition, in this paper we propose a novel adaptive multilayer dictionary learning (AMDL) algorithm. An adaptive dictionary generation model is creatively proposed and designed, based on which each sample is able to learn the idiosyncratic dictionary, thus reducing the interference between different material samples. A method of learning label groups for each test sample is proposed to ensure that the test sample learns the correct material category. Support vector guided dictionary learning with locality constraints algorithm is proposed to achieve accurate texture classification. The results of validation experiments on the LMT-108 dataset show that the AMDL algorithm proposed in this paper obtains a better performance in one-shot texture recognition with a recognition rate of 96.71% compared with other similar dictionary learning algorithms.