基于结构化组稀疏的图像标注

Image annotation based on structured grouping sparsity

图像中存在颜色、形状和纹理等全局特征以及LBP和SIFT等局部特征,这些异构特征之间存在明显的结构信息。不同视觉特征在表示特定高层语义时重要程度不同,因此,正确的特征选择对于图像标注来说具有十分重要的意义。为了充分利用异构特征之间的结构组效应,提出了一种基于组稀疏的高维特征选择算法及其在图像标注中的应用。通过与其他三种算法在图像标注上的性能对比,证明该算法能得到更优的图像标注结果。

The heterogeneous features can describe various aspects of visual characteristics of images, such as global features （color, shape and texture） or local features （SIFT and LBP）. Different heterogeneous features have different structural information. Different groups of heterogeneous features have different intrinsic discriminative power to characterize the semantics inside images. Therefore, to select the right features is of great significance for image annotation. In order to effectively utilize the structural grouping effect among heterogeneous-visual features, a high-dimensional feature selection method based on structured grouping sparsity is proposed, and its application in image annotation is introduced. Comparing with the performance of other three algorithms in image annotation, it is proved that the proposed algorithm can get better image annotation results.