From Label Maps to Label Strokes: Semantic Segmentation for Street Scenes from Incomplete Training Data

Shengqi Zhu, Yiqing Yang, Li Zhang; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 468-475

Abstract


This paper proposes a novel image parsing framework to solve the semantic pixel labeling problem from only label strokes. Our framework is based on a network of voters, each of which aggregates both a self voting vector and a neighborhood context. The voters are parameterized using sparse convex coding. To efficiently learn the parameters, we propose a regularized energy function that propagates label information in the training data while taking into account of context interaction and a backward composition algorithm for efficient gradient computation. Our framework is capable of handling label strokes and is scalable to a code book of millions of bases. Our experiment results show the effectiveness of our framework on both synthetic examples and real world applications.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhu_2013_ICCV_Workshops,
author = {Shengqi Zhu and Yiqing Yang and Li Zhang},
title = {From Label Maps to Label Strokes: Semantic Segmentation for Street Scenes from Incomplete Training Data},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}