IEEE Trans Neural Netw Learn Syst. 2013 May;24(5):709-22. doi: 10.1109/TNNLS.2013.2238682.
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multilabel structure in the output space, we introduce multiview vector-valued manifold regularization (MV(3)MR) to integrate multiple features. MV(3)MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conduct extensive experiments on two challenging, but popular, datasets, PASCAL VOC' 07 and MIR Flickr, and validate the effectiveness of the proposed MV(3)MR for image classification.
在计算机视觉中，用于分类的图像数据集通常与多个标签相关联，并由多个视图组成，因为每张图像可能包含多个对象（例如行人、自行车和树），并且可以通过多个视觉特征（例如颜色、纹理和形状）进行适当的描述。目前，现有的工具要么忽略标签之间的关系，要么忽略视图之间的互补性。受向量值函数构建矩阵值核以探索输出空间中多标签结构成功的启发，我们引入了多视图向量值流形正则化（MV(3)MR）来集成多个特征。MV(3)MR 利用不同特征的互补性，并在流形正则化的主题下发现不同特征之间共享的紧致支持的内在局部几何结构。我们在两个具有挑战性但流行的数据集 PASCAL VOC' 07 和 MIR Flickr 上进行了广泛的实验，并验证了所提出的 MV(3)MR 对图像分类的有效性。
