一种新的多尺度深度学习图像语义理解方法研究

如何在深度学习中融合 图像的多尺度信息,是基于深度学习的视觉算法需要解决的一个关键问题。本文提出一种基 于多尺度交替 迭代训练的深度学习方法,并应用于图像的语义理解。算法采用卷积神经网络(CNN)从原始 图像中提取稠密性特征 来编码以每个像素为中心的矩形区域,将多个尺度图像交替迭代训练,能够捕获不同尺度下 的纹理、颜色和 边缘等重要信息。在深度学习提取特征分类结果的基础上,提出了一种结合超像素分割的方 法,统计超像 素块的主导类别,来校正分类错误的像素类别,同时描绘出目标区域边界轮廓,完成最终的 语义理解。在Stanford Background Dataset 8类数据集上验证了本文方法的有效性,准确 率达到77.4%。

A new multi-scale image semantic understanding method based on deep learning

How to fuse multi-scale information o f image in deep learning is a key problem to be solved.To solve these problems,this paper proposed a deep learning method base d on multi-scale iterative training for image semantic understanding.The algorithm uses the convolution neu ral network (CNN) to extract dense feature vectors from raw pixel for encoding regions centered on each pixel.The multi-scale iterative training captures different scales of textures,colors,edges and other important informa tion.A new method combined with superpixel segmentation is proposed,to estimate the leading category of superpi xel block and to correct the pixel classification error.It can depict the outline of the target area boundary a nd complete the final semantic understanding.The experiments on Stanford Background Dataset-8verify the effectiveness of the proposed method,and the accuracy rate is 77.4%.