自适应稀疏编码融合的非刚性三维模型分类算法  

Non-rigid 3D Model Classification Algorithm Based on Adaptive Sparse Coding Fusion

作　　者：韩丽[1] 刘书宁 徐圣斯 朴京钰 Han Li;Liu Shuning;Xu Shengsi;Piao Jingyu(School of Computer Science and Information Technology,Liaoning Normal University,Dalian 116081)

机构地区：[1]辽宁师范大学计算机与信息技术学院

出　　处：《计算机辅助设计与图形学学报》2019年第11期1898-1907,共10页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61702246);辽宁省社科规划基金(2018lslktyb-084);大连市科技创新基金(2019J12GX038);大连市科技创新基金(2019J12GX038)

摘　　要：针对传统的三维模型单一特征识别精度低问题,提出一种自适应稀疏编码融合的非刚性三维模型分类算法.首先提取模型的平均测地线特征、热核特征、形状直径函数特征,以构造互补的多特征形状描述;其次利用特征袋模型构造AGD-BoF, HKS-BoF, SDF-BoF特征向量,通过随机样本建立特征权值矩阵;最后利用特征权值矩阵与稀疏优化编码自适应融合,并采用Softmax分类算法实现非刚性模型的有效分类.通过在非刚性数据集SHREC10与SHREC11上的综合实验表明,自适应稀疏编码融合的分类算法具有更高的识别准确率及较强的鲁棒性.Aiming at the issue of low accuracy of single feature model in traditional 3 D shape recognition methods, this paper proposes an adaptive sparse coding fusion algorithm for non-rigid 3 D shape classification. This method first extracts the low-level features of average geodesic distance(AGD), heat kernel signature(HKS), and shape diameter function(SDF) to construct complementary shape descriptors. Secondly, the AGD-BoF, HKS-BoF, and SDF-BoF are generated respectively based on the bag-of-feature(BoF) model. And then we build a weighted matrix via random training. Finally, we adaptively create a fusion between the optimized sparse coding and weighted matrix which is used to achieve effective classification by using the Softmax classifier. A series experiments on two non-rigid databases SHREC10 and SHREC11 have shown that our proposed algorithm has better classification accuracy and stronger robustness.

关 键 词：特征提取 特征袋模型 稀疏编码 形状分类 非刚性三维模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]