基于反卷积特征学习的图像语义分割算法

随着深度学习的发展，语义分割任务中许多复杂的问题得以解决，为图像理解奠定了坚实的基础.本文算法突出表现在两个方面，其一是利用反卷积网络，对卷积网络中不同深度的卷积层提取到的多尺度特征进行融合，之后再次通过反卷积操作对融合后的特征图进行上采样，将其放大到原图像的大小，最后对每个像素进行语义类别的预测.其二为了提升本文网络结构的性能，提出一种新的数据处理方式，批次中心化算法.经过实验验证，本文算法在SIFT-Flow数据集上语义分割的平均准确率达到45.2%，几何分割的准确率达到96.8%，在PASCAL VOC2012数据集上语义分割的平均准确率达到73.5%.

Image Semantic Segmentation Algorithm Based on Deconvolution Feature Learning

With the development of deep learning, many complex problems in semantic segmentation tasks are solved, which lays a solid foundation for image understanding. The proposed algorithm highlights two aspects. Firstly, our algorithm fuses multi-scale features from different levels of deep convolutional network by using multi-level deconvolution network. Then our algorithm upsamples these feature maps by deconvolution, meanwhile zooms them up to the original image size to predict semantic categories pixel-to-pixel. The second one, we propose a new method for data processing which is batch centralization algorithm, in order to improve the performance of network structure in this study. Through experimental verification, the mean IoU of semantic segmentation on the SIFT-Flow dataset reaches 45.2%, and the accuracy of geometric segmentation reaches 96.8%. The mean IoU of semantic segmentation on the PASCAL VOC2012 dataset reaches 73.5%.