School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
IEEE Trans Image Process. 2012 Nov;21(11):4508-21. doi: 10.1109/TIP.2012.2206040. Epub 2012 Jun 26.
In this paper, we propose a novel supervised nonnegative matrix factorization-based framework for both image classification and annotation. The framework consists of two phases: training and prediction. In the training phase, two supervised nonnegative matrix factorizations for image descriptors and annotation terms are combined to identify the latent image bases, and to represent the training images in the bases space. These latent bases can capture the representation of the images in terms of both descriptors and annotation terms. Based on the new representation of training images, classifiers can be learnt and built. In the prediction phase, a test image is first represented by the latent bases via solving a linear least squares problem, and then its class label and annotation can be predicted via the trained classifiers and the proposed annotation mapping model. In the algorithm, we develop a three-block proximal alternating nonnegative least squares algorithm to determine the latent image bases, and show its convergent property. Extensive experiments on real-world image data sets suggest that the proposed framework is able to predict the label and annotation for testing images successfully. Experimental results have also shown that our algorithm is computationally efficient and effective for image classification and annotation.
在本文中，我们提出了一种新颖的基于监督非负矩阵分解的框架，用于图像分类和标注。该框架由两个阶段组成：训练和预测。在训练阶段，将用于图像描述符和标注项的两个监督非负矩阵分解相结合，以识别潜在的图像基，并在基空间中表示训练图像。这些潜在的基可以捕获图像在描述符和标注项方面的表示。基于训练图像的新表示，可以学习和构建分类器。在预测阶段，首先通过求解线性最小二乘问题，将测试图像表示为潜在基，然后通过训练的分类器和提出的标注映射模型，预测其类别标签和标注。在算法中，我们开发了一个三模块的近端交替非负最小二乘算法来确定潜在的图像基，并展示了其收敛性。在真实图像数据集上的广泛实验表明，所提出的框架能够成功地预测测试图像的标签和标注。实验结果还表明，我们的算法对于图像分类和标注具有计算效率和有效性。
