Image Feature Extraction Based on Complex Network and Multi-Feature Fusion Schemes Exploration in CBIR
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.
文章引用: 高剂斌 , 李裕梅 , 张慧娜 (2015) 基于复杂网络的图像特征提取及多特征融合方案探究。 图像与信号处理， 4， 101-110. doi: 10.12677/JISP.2015.44012
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