SNMFCA: Supervised NMF-Based Image Classification and Annotation
SNMFCA: Supervised NMF-Based Image Classification and Annotation
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SNMFCA：基于 NMF 的监督图像分类和注释
DOI：
10.1109/tip.2012.2206040
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发表时间：
2012-11-01
影响因子：
10.6
通讯作者：
Ng, Michael K.
中科院分区：
文献类型：
--
作者：
Jing, Liping;Zhang, Chao;Ng, Michael K.
在本文中，我们提出了一种新的监督非负矩阵分解为基础的框架，图像分类和注释。该框架包括两个阶段：训练和预测。在训练阶段，两个监督的非负矩阵分解的图像描述符和注释条款相结合，以确定潜在的图像基地，并表示在基地空间的训练图像。这些潜在的基础可以捕获的描述符和注释条款方面的图像的表示。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用潜在基来表示测试图像，然后通过训练好的分类器和提出的注释映射模型来预测其类标签和注释。在该算法中，我们提出了一种三块近似交替非负最小二乘算法来确定潜像基，并证明了它的收敛性。在真实图像数据集上的大量实验表明，该框架能够成功地预测测试图像的标签和注释。实验结果也表明，我们的算法是计算效率和有效的图像分类和注释。
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.