Sparse Reconstruction for Weakly Supervised Semantic Segmentation

Ke Zhang, Wei Zhang, Yingbin Zheng, Xiangyang Xue

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Introduction

For better understanding and management of massive images, semantic segmentation aims to identify the semantic label of pixels of images, i.e., assign each pixel in an image to one of pre-defined semantic categories.

Most state-of-the-art rely on training set that every pixel of images are annotated, which is time-consuming and limited in resources.

Challenge

The main difficulty of faced by the weakly supervised semantic segmentation task:

Motivation

We use "evaluation" of classifiers instead of training a classifier

Substantiation is given by the costs of samples reconstructed by the subspace of each semantic class

New criterion for evaluation of classifiers:

Our Approach

Iterative Merge Update:

Experiments

Conclusion

Reference

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