基于混合距离的复杂网络形状描述方法 - 中国图象图形学报.doc

中图法分类号: TP391.4 文献标识码:A 文章编号: - (2014 ) - - 论文引用格式： 改进复杂网络模型的形状特征提取阮瑞, 江波, 汤进, 罗斌1，2 1安徽大学 计算机科学与技术学院, 合肥 中国 2306012安徽省工业图像处理与分析重点实验室, 合肥 中国 230039 摘 要： 关键字：复杂网络；相对一致性距离；内部距离；动态演化 hape feature extraction using complex network?model Ruan rui1, Jiang bo1, Tang jin1, 2, Luo bin1, 2 1 School of Computer Science and Technology, Anhui University, Hefei 230601, China； 2 Key Lab of Industrial Image Processing Analysis of Anhui Province, Hefei 230039, China Abstract：Objective:successfully used in shape analysis and recognition. However, traditional Euclidean distance based complex network is usually sensitive to the non-rigid transformation of the shape image. Method: To overcome this problem, in this paper, a novel shape feature extraction method based on complex network model and relative coherent distance is proposed. Firstly, an initial complex network is constructed with nodes corresponding to the boundary points and edges allocated with relative coherent distance as weights existing between each node pairs. Then, this initial network is threshold evolved to generate a series of sub-networks. At last, some topological features are extracted from these sub-networks to generate the feature descriptors for the shape image. Result: Promising experimental results on classification and retrieval show that the proposed method has strong capability in discriminating and recognizing variety of object shapes. Conclusion: Comparing with the traditional distances, the relative coherent distance is more robust to the shape non-rigid and elastic transformations. Keywords: complex network; relative coherent distance; inner distance; dynamic evolution 0引言 形状特征作为物体最基本的视觉特征之一，在计算机视觉和模式识别中有着重要的作用[1]。目前关于形状描述的方法]有很多，主要可分为两类：一类是基于区域的形状描述方法，另一类是基于边界轮廓的形状描述方法。在基于区域的形状描述方法中，Zhu等人[]提出了分支限界法进行形状的骨架图匹配；Siddiqi等人[]提出了Shock图的形状表示方法；Bai等人[]提出了基于路径相似的骨架图匹配方法等等。在基于形状边界轮廓的描述方法中，傅里叶描述子[]、小波描述子[]都是经典有效的描述算法。此外，Belongie等人[]提出了形状上下文(Shape Context)形状描述方法；Ling等[]提出了内部距离的形状上下文表示方法；Hu等[]进一步