有效学习丰富的表征信息在RGB-D目标识别任务中至关重要, 是实现高泛化性能的关键。针对卷积神经网络训练时间长的问题, 提出了一种混合卷积自编码极限学习机(HCAE-ELM)结构, 包括卷积神经网络(CNN)和自编码极限学习机(AE-ELM), 该结构合并了CNN的有效性和AE-ELM快速性的优点。它使用卷积层和池化层分别从RGB和深度图来有效提取低阶特征, 然后在共享层合并两种模型特征, 输入到自编码极限学习机中以得到高层次的特征, 最终的特征使用极限学习机(ELM)进行分类, 以获得更好的快速泛化能力。文中在标准的RGB-D数据集上进行了评估测试, 其实验结果表明, 相比较深度学习和其他的ELM方法, 文中的混合卷积自编码极限学习机模型取得了良好的测试准确率, 并且有效地缩减了训练时间。

Learning rich representations efficiently plays an important role in RGB-D object recognition task, which is crucial to achieve high generalization performance. For the long training time of convolutional neural networks, a Hybrid Convolutional Auto-Encoder Extreme Learning Machine Structure(HCAE-ELM) was put forward which included Convolutional Neural Network(CNN) and Auto-Encoder Extreme Learning Machine(AE-ELM), which combined the power of CNN and fast training of AE-ELM. It used convolution layers and pooling layers to effectively abstract lower level features from RGB and depth images separately. And then, the shared layer was developed by combining these features from each modality and fed to an AE-ELM for higher level features. The final abstracted features were fed to an ELM classifier, which led to better generalization performance with faster learning speed. The performance of HCAE-ELM was evaluated on RGB-D object dataset. Experimental results show that the proposed method achieves better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.