National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Image Process. 2013 Mar;22(3):860-71. doi: 10.1109/TIP.2012.2219543. Epub 2012 Sep 21.
Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0) norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0) norm optimization into a convex l(1) norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.
基于稀疏表示 (SR) 的最新技术在高级视觉识别方面表现出了很有前景的性能，例如在遮挡和其他稀疏损坏下的高精度人脸识别。该领域的大多数研究都集中在使用原始图像像素的分类算法上，很少有研究提出利用量化的视觉特征，如流行的词袋特征抽象。在这种情况下，除了固有量化误差之外，由于视觉遮挡和特征点误检等因素引起的与视觉词分配相关的歧义也是量化表示密集损坏的主要原因。密集损坏会通过扭曲稀疏重建系数的模式来危及决策过程。在本文中，我们旨在利用 SR 消除损坏并实现稳健的图像分析。为此，我们引入了两种转移过程（歧义转移和误检转移）来考虑所讨论的两种主要的损坏源。通过合理假设这两种失真过程的稀有性，我们在原始基于 SR 的重建目标上增加了转移项的 l(0)范数正则化，以鼓励稀疏性，从而抑制密集失真/转移。在计算上，我们将非凸 l(0)范数优化松弛为凸 l(1)范数优化问题，并采用加速近端梯度法优化可证明收敛的更新过程。在四个基准数据集（Caltech-101、Caltech-256、Corel-5k 和 CMU 姿势、光照和表情）上进行的广泛实验表明了消除量化损坏的必要性以及所提出框架的各种优势。
