摘要:
图像形状特征的提取是图像检索中重要的研究内容，本文提出一种基于复杂网络模型的图像形状特征提取方法。提取图像的SIFT关键点，在此基础上进行分块处理并逐一在每个子图上面构建复杂网络初始模型，利用最小生成树分解的方法对网络进行动态演化，提取不同子图不同时刻下的网络特征作为形状特征。本文将形状特征与颜色特征、纹理特征进行融合，通过实验比较，说明此融合方案在CBIR应用中确实具有优势。
Abstract:
Image shape feature’s extraction is an important research content in content-based image retrieval, and an image shape feature extraction method by using complex network model is proposed in this paper. First, SIFT keypoints of an image are extracted, and then the image is divided into blocks such that the initial complex network model can be built in each block respectively. After that, minimum spanning tree decomposition method is used for the network’s dynamic evolution, and the network features at different moments in different blocks are extracted as the image’s shape features. Furthermore, the shape features are combined with the color and texture features and a kind of fusion feature is obtained. By experiment results comparison, it shows that the fusion feature does have advantages in CBIR.