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(PAMI 2017) Segnet:A deep convolutional encoder-decoder architecture for image segmentation

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Keyword [Pooling Indices] [SegNet]

Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12): 2481-2495.


1. Overview

1.1. Motivation

  • Due to the lack the good encoding techniques (poor ability to delineate boundaries), existing method use CRF to increase accuracy

In this paper. it proposed SegNet

  • In decoder, using upsampling according to the pooling indices of the encoder’s corresponding max-pooling layer.


  • Upsample. get sparse feature map

  • Conv. get dense feature map



  • Eliminate the need for learning to upsample

  • Less memory in the inference time


1.2. Comparison

  • Traditional method
  • DeepLab-LargeFOV
  • DeconvNet


1.3. Dataset

  • CamVid road scenes
  • SUN RGB-D indoor scenes
  1. Hand Engineered Feature
  2. DNN
  • Random Forest
  • Boosting
  • Smooth classifier by CRF
  • Region Proposal. do not exploit co-occurrence of object or other spatial-context
  • RGBD segmentation
  • CRF-RNN. using RNN mimic the charp boundary delineation capabilities of CRF; can be appended to any deep segmentation architecture
  • Multi-scale. image or feature
  • DeconvNet
  • DecoupledNet

SegNet is inspired by the unsupervised feature learning architecture proposed by Ranzato.

1.5. Experiments

  • Best performance is achieved when encoder feature maps are stored in full
  • When memory during inference is constrained, the compressed form (dimensionality reduction, max-pooling indices) can improve performance
  • Larger decoder increase performance for a given encoder