基于潜在狄利克雷分配的图像多层视觉表示方法

摘要

Image layer visual representation has been currently used in computer vision field,but it is difficult for feedforward image multilayer visual representation methods to deal with local ambiguities.An image multilayer visual representation method based on Latent Dirichlet Allocation(LDA) named LDA-IMVR was proposed.It derived a recursive generative model of LDA by implementing recursive probabilistic decomposition process.Meanwhile,it learned and deduced all layers of the hierarchy together,and improved classification and learning performance by using feed-back style.The approach was tested on Caltech 101 dataset.The experimental results show that the proposed method improves classification performance of objects compared with related hierarchical approaches,and it achieves better results in learned components and image patches visualization.%针对前馈型图像多层视觉表示方法难以处理局部模糊情况,提出一种基于潜在狄利克雷分配(LDA)的图像多层视觉表示方法——LDA-IMVR.通过递归的概率分解方式,获得LDA的递归生成模型；同时,通过学习和推断多层结构的所有分层,以及利用反馈方式来提高分类学习性能.在Caltech 101数据集上的实验结果表明,与相关的多层视觉表示方法比较,LDA-IMVR提高了数据对象的分类性能,并且在分量学习和图像特征区域可视化方面也得到了较好的效果.