Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China.
IEEE Trans Image Process. 2013 Feb;22(2):523-36. doi: 10.1109/TIP.2012.2218825. Epub 2012 Sep 13.
It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.
使用少量标记样本对具有多个标签的图像进行分类是一项重大挑战。一种选择是为每个标签学习一个二进制分类器，并通过探索数据分布的潜在几何结构，使用流形正则化来提高分类性能。然而，当来自多个概念的图像由高维视觉特征表示时，这种方法在实践中表现不佳。因此，流形正则化不足以控制模型的复杂度。在本文中，我们提出了一种流形正则化多任务学习 (MRMTL) 算法。MRMTL 通过利用这些任务的共同结构，学习多个分类任务共享的判别子空间。它有效地控制了模型的复杂度，因为不同的任务限制了彼此的搜索量，并且流形正则化确保共享假设空间中的函数沿着数据流形平滑。我们在 PASCAL VOC'07 数据集上进行了广泛的实验，该数据集有 20 个类，以及 MIR 数据集上有 38 个类，通过将 MRMTL 与流行的图像分类算法进行比较。结果表明，MRMTL 对图像分类有效。
