Department of Statistics, Rutgers University, Newark, NJ 08854, USA.
IEEE Trans Image Process. 2012 Oct;21(10):4349-60. doi: 10.1109/TIP.2012.2205006. Epub 2012 Jun 18.
We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.
我们解决了具有多个特征和/或多个实例的视觉分类问题。受最近多任务联合协变量选择成功的启发，我们将此问题表述为多任务联合稀疏表示模型，以结合多个特征和/或实例的优势进行识别。我们利用联合稀疏诱导范数来强制多个表示向量之间的类级联合稀疏模式。所提出的模型可以通过近端梯度方法进行有效优化。此外，我们将我们的方法扩展到特征用核矩阵描述的设置中。然后，我们将我们的方法应用于两个视觉分类的应用中：1）融合多个核特征进行目标分类，2）在具有查询图像集合的视频中进行稳健人脸识别。在具有挑战性的真实世界数据集上的广泛实验表明，该方法在各自的应用中具有竞争力。
