论文标题
通过基于子空间分解的深度估计来提高遮挡图像分类
Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
论文作者
论文摘要
即使对于尖端深度学习技术，部分遮挡图像的分类也是一个高度挑战的计算机视觉问题。为了实现遮挡图像的强大图像分类，本文提出了一种使用基于子空间分解估计（SDBE）的新方案。提出的基于SDBE的分类方案首先采用基本卷积神经网络来提取深度特征向量（DFV），然后利用SDBE计算原始无咬合图像的DFV进行分类。 SDBE是通过将遮挡图像的DFV投影到canterme误差字典（OED）的线性跨度的类词典（CD）的线性跨度上的。 CD和OED分别是通过串联训练集的DFV和额外的图像对的遮挡误差向量来构建的。本文研究了SDBE的两个实现：$ l_1 $ - norm和平方$ l_2 $ norm正规化最小二乘估计值。通过在ILSVRC2012培训集中进行预培训的RESNET-152作为基本网络，在CalTech-101和ILSVRC2012数据集中对基于SBDE的基于SBDE的分类方案进行了广泛的评估。广泛的实验结果表明，拟议的基于SDBE的方案大大提高了遮挡图像的分类精度，并且在ILSVRC2012数据集中的分类准确性$ 20 \％$ bactlusion的分类准确性在$ 20 \％$ bclusion的分类准确性上提高约为$ 22.25 \％。
Classification of partially occluded images is a highly challenging computer vision problem even for the cutting edge deep learning technologies. To achieve a robust image classification for occluded images, this paper proposes a novel scheme using subspace decomposition based estimation (SDBE). The proposed SDBE-based classification scheme first employs a base convolutional neural network to extract the deep feature vector (DFV) and then utilizes the SDBE to compute the DFV of the original occlusion-free image for classification. The SDBE is performed by projecting the DFV of the occluded image onto the linear span of a class dictionary (CD) along the linear span of an occlusion error dictionary (OED). The CD and OED are constructed respectively by concatenating the DFVs of a training set and the occlusion error vectors of an extra set of image pairs. Two implementations of the SDBE are studied in this paper: the $l_1$-norm and the squared $l_2$-norm regularized least-squares estimates. By employing the ResNet-152, pre-trained on the ILSVRC2012 training set, as the base network, the proposed SBDE-based classification scheme is extensively evaluated on the Caltech-101 and ILSVRC2012 datasets. Extensive experimental results demonstrate that the proposed SDBE-based scheme dramatically boosts the classification accuracy for occluded images, and achieves around $22.25\%$ increase in classification accuracy under $20\%$ occlusion on the ILSVRC2012 dataset.
