---
_id: '1533'
abstract:
- lang: eng
text: This paper addresses the problem of semantic segmentation, where the possible
class labels are from a predefined set. We exploit top-down guidance, i.e., the
coarse localization of the objects and their class labels provided by object detectors.
For each detected bounding box, figure-ground segmentation is performed and the
final result is achieved by merging the figure-ground segmentations. The main
idea of the proposed approach, which is presented in our preliminary work, is
to reformulate the figure-ground segmentation problem as sparse reconstruction
pursuing the object mask in a nonparametric manner. The latent segmentation mask
should be coherent subject to sparse error caused by intra-category diversity;
thus, the object mask is inferred by making use of sparse representations over
the training set. To handle local spatial deformations, local patch-level masks
are also considered and inferred by sparse representations over the spatially
nearby patches. The sparse reconstruction coefficients and the latent mask are
alternately optimized by applying the Lasso algorithm and the accelerated proximal
gradient method. The proposed formulation results in a convex optimization problem;
thus, the global optimal solution is achieved. In this paper, we provide theoretical
analysis of the convergence and optimality. We also give an extended numerical
analysis of the proposed algorithm and a comprehensive comparison with the related
semantic segmentation methods on the challenging PASCAL visual object class object
segmentation datasets and the Weizmann horse dataset. The experimental results
demonstrate that the proposed algorithm achieves a competitive performance when
compared with the state of the arts.
author:
- first_name: Wei
full_name: Xia, Wei
last_name: Xia
- first_name: Csaba
full_name: Domokos, Csaba
id: 492DACF8-F248-11E8-B48F-1D18A9856A87
last_name: Domokos
- first_name: Junjun
full_name: Xiong, Junjun
last_name: Xiong
- first_name: Loongfah
full_name: Cheong, Loongfah
last_name: Cheong
- first_name: Shuicheng
full_name: Yan, Shuicheng
last_name: Yan
citation:
ama: Xia W, Domokos C, Xiong J, Cheong L, Yan S. Segmentation over detection via
optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for
Video Technology. 2015;25(8):1295-1308. doi:10.1109/TCSVT.2014.2379972
apa: Xia, W., Domokos, C., Xiong, J., Cheong, L., & Yan, S. (2015). Segmentation
over detection via optimal sparse reconstructions. IEEE Transactions on Circuits
and Systems for Video Technology. IEEE. https://doi.org/10.1109/TCSVT.2014.2379972
chicago: Xia, Wei, Csaba Domokos, Junjun Xiong, Loongfah Cheong, and Shuicheng Yan.
“Segmentation over Detection via Optimal Sparse Reconstructions.” IEEE Transactions
on Circuits and Systems for Video Technology. IEEE, 2015. https://doi.org/10.1109/TCSVT.2014.2379972.
ieee: W. Xia, C. Domokos, J. Xiong, L. Cheong, and S. Yan, “Segmentation over detection
via optimal sparse reconstructions,” IEEE Transactions on Circuits and Systems
for Video Technology, vol. 25, no. 8. IEEE, pp. 1295–1308, 2015.
ista: Xia W, Domokos C, Xiong J, Cheong L, Yan S. 2015. Segmentation over detection
via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems
for Video Technology. 25(8), 1295–1308.
mla: Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”
IEEE Transactions on Circuits and Systems for Video Technology, vol. 25,
no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.
short: W. Xia, C. Domokos, J. Xiong, L. Cheong, S. Yan, IEEE Transactions on Circuits
and Systems for Video Technology 25 (2015) 1295–1308.
date_created: 2018-12-11T11:52:34Z
date_published: 2015-08-01T00:00:00Z
date_updated: 2021-01-12T06:51:26Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/TCSVT.2014.2379972
intvolume: ' 25'
issue: '8'
language:
- iso: eng
month: '08'
oa_version: None
page: 1295 - 1308
publication: IEEE Transactions on Circuits and Systems for Video Technology
publication_status: published
publisher: IEEE
publist_id: '5638'
quality_controlled: '1'
scopus_import: 1
status: public
title: Segmentation over detection via optimal sparse reconstructions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2015'
...