针对现有基于结构元描述的图像特征提取算法缺少连续像素或结构元的相关性描述，对图像特征的区分能力不足的问题。通过定义新的结构元和自适应向量融合模型，并引入连通粒概念，提出一种加权量化方法对图像目标和背景进行自适应融合。首先根据视觉选择特性定义9种新的结构元，并且构建了连通粒属性及分层统计模型；然后通过颜色转换和结构元匹配生成相应的映射子图，从中提取统计结构元和连通性特征向量；最后利用自适应向量融合模型把各分量合并为一组特征向量用于图像检索。在3个 Corel 数据集上的实验结果表明，与其他算法相比，文中方法性能更稳定，能达到更高的检索精度；该方法既能描述图像的全局特征，又能反映图像的局部细节信息。%Existing algorithms based on structural descriptor are not accurate enough to discriminate the image features, because they lack the correlation description of continuous pixels or structural elements. To address this problem, this paper presents a novel weighted quantization method, which can adaptively integrate images object features and background features into one image histogram. The proposed method includes the new structure elements definition, adaptive vector fusion model and connected granule concept. Firstly, based on the visual se-lection characteristics, nine kinds of new structure elements are defined. The connected granule' attributes are given and the hierarchical statistical model is constructed. Secondly, the corresponding mapping sub-graphs are generated by color transformation and structure elements matching. Meanwhile, the feature vectors of statistical structure elements and connectivity are extracted. Finally, a set of feature vectors are obtained by utilizing adap-tive vector fusion model for image retrieval. Extensive experiments on three Corel-datasets demonstrate that the novel algorithm performs better than several state-of-the-art image feature representation methods. The proposed method not only captures the global features of images, but also reflects the local details of images.
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