0、实验结果
1、定性比较——在CamVidday和dusk测试样品上的实验结果
Results on CamVidday and dusk test samples，几个测试样的图像，包括白天和傍晚。对比的算法包括SegNet、FCN、FCN(learn deconv)、DeconvNet算法，只有SegNet算法给出了比较好的分割效果。
2、定量比较——在CamVid11道路类分割问题上，将SegNet与传统方法进行定量比较
Quantitative comparisons of SegNet with traditional methods on the CamVid11 road class segmentation problem
SegNet outperforms all the other methods, including those using depth, video and/or CRF’s on the majority of classes.
SegNet的单独IU得分都比较高，最后的mean IU可达到60.1%。都优于所有其他方法，包括那些在大多数类上使用深度、视频和/或CRF的方法。
1、SegNet算法的关键思路
1、An illustration of the SegNet architecture. There are no fully connected layers and hence it is only convolutional. A decoder upsamples its input using the transferred pool indices from its encoder to produce a sparse feature map(s). It then performs convolution with a trainable filter bank to densify the feature map. The final decoder output feature maps are fed to a soft-max classifier for pixel-wise classification.
2、An illustration of SegNet and FCN [2] decoders. a, b, c, d correspond to values in a feature map. SegNet uses the max pooling indices to upsample (without learning) the feature map(s) and convolves with a trainable decoder filter bank. FCN upsamples by learning to deconvolve the input feature map and adds the corresponding encoder feature map to produce the decoder output. This feature map is the output of the max-pooling layer (includes sub-sampling) in the corresponding encoder. Note that there are no trainable decoder filters in FCN.
SegNet图像分割算法的架构详解
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SegNet图像分割算法的案例应用
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