结合组稀疏效应和多核学习的图像标注

Image Annotation by the Multiple Kernel Learning with Group Sparsity Effect

图像中存在的纹理、颜色和形状等异构视觉特征,在表示特定高层语义时所起作用的重要程度不同.为了在图像标注过程中更加有效地利用这些异构特征,提出了一种基于组稀疏(group sparsity)的多核学习方法(multiplekernel learning with group sparsity,简称MKLGS),为不同图像语义选择不同的组群特征.MKLGS先将包含多种异构特征的非线性图像数据映射到一个希尔伯特空间,然后利用希尔伯特空间中的核函数以及组LASSO(groupLASSO)对每个图像类别选择最具区别性特征的集合,最终训练得到分类模型对图像进行标注.通过与目前其他图像标注算法进行对比,实验结果表明,基于组稀疏的多核学习方法在图像标注中能取得很好的效果.

Since different kinds of heterogeneous features （such as color, shape and texture） in image shave different intrinsic discriminative power for image understanding, this paper proposes a multiple kernel learning with group sparsity （MKLGS） to select groups of discriminative features for image annotation to effectively utilize those heterogeneous visual features. Given each image label, the MKLGS method embeds the nonlinearity image data with discriminative features into a Hilbert space, and then utilizes the kernel function in the Hilbert space and group LASSO to select groups of discriminative features. Finally, a classification model can be trained for image annotation. In comparison to other image annotation algorithms, experiments show that the proposed MKLGS for imageannotation achieves a better performance.