Unsupervised texture segmentation based on latent topic assignment
Unsupervised texture segmentation based on latent topic assignment
复制标题
DOI：
10.1117/1.jei.22.1.013026
复制
发表时间：
2013
影响因子：
1.1
通讯作者：
Haoxiang Feng;Zhi-guo Jiang;Jun Shi
中科院分区：
文献类型：
--
作者：
Haoxiang Feng;Zhi-guo Jiang;Jun Shi
抽象的。利用潜在Dirichlet分配(LDA)模型，提出了一种有效的无监督纹理分割方法。LDA是一种生成式主题模型，能够分层组织包括文本和图像在内的离散数据。本文将纹理基元与LDA主题相结合，提出了一种新的纹理模型。该模型能够提取纹理基元的特征特征，并根据它们的共现频率将它们分组为一个主题。这里，特征描述符是多个大小的类Haar特征的连接。通过识别相应主题分配图中的同质区域，最终获得图像的片段。给出了合成纹理马赛克图像、遥感图像和自然景物图像的评价结果。
Abstract. We present an effective solution for unsupervised texture segmentation by taking advantage of the latent Dirichlet allocation (LDA) model. LDA is a generative topic model that is capable of hierarchically organizing discrete data including texts and images. We propose a new texture model by connecting texture primitives to the topic of LDA. The model is able to extract the characteristic features of a texture primitive and group them into a topic based on their frequencies of co-occurrence. Here, the feature descriptor is the connection of Haar-like features of multiple sizes. The segments of an image are finally obtained by identifying the homogeneous regions in the corresponding topic assignment map. The evaluation results for synthetic texture mosaics, remote sensing images, and natural scene images are illustrated.