Visual classification with multi-task joint sparse representation
Visual classification with multi-task joint sparse representation
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DOI：
10.1109/cvpr.2010.5539967
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Xiao-Tong Yuan;Shuicheng Yan
中科院分区：
文献类型：
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作者：
Xiao-Tong Yuan;Shuicheng Yan
我们解决的问题，计算联合稀疏表示的视觉信号在多个基于内核的表示。这样的问题自然出现在监督视觉识别应用中，其中一个目的是从尽可能少的训练对象中重建具有多个特征的测试样本。我们将该问题的线性版本转换为多任务联合协变量选择模型[15]，该模型可以通过核化加速邻近梯度方法非常有效地优化。此外，该方法的两个内核视图扩展提供了处理的情况下，描述符和相似性函数的形式的核矩阵。然后，我们研究了我们的算法的特征组合的两个应用：1）融合灰度和LBP特征的人脸识别，2）结合多个内核的对象分类。在具有挑战性的真实世界数据集上的实验结果表明，我们提出的算法的特征组合能力是最先进的多核学习方法的竞争力。
We address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual recognition applications where one aims to reconstruct a test sample with multiple features from as few training subjects as possible. We cast the linear version of this problem into a multi-task joint covariate selection model [15], which can be very efficiently optimized via ker-nelizable accelerated proximal gradient method. Furthermore, two kernel-view extensions of this method are provided to handle the situations where descriptors and similarity functions are in the form of kernel matrices. We then investigate into two applications of our algorithm to feature combination: 1) fusing gray-level and LBP features for face recognition, and 2) combining multiple kernels for object categorization. Experimental results on challenging real-world datasets show that the feature combination capability of our proposed algorithm is competitive to the state-of-the-art multiple kernel learning methods.