---
_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. <i>IEEE Transactions on Circuits and Systems for
    Video Technology</i>. 2015;25(8):1295-1308. doi:<a href="https://doi.org/10.1109/TCSVT.2014.2379972">10.1109/TCSVT.2014.2379972</a>
  apa: Xia, W., Domokos, C., Xiong, J., Cheong, L., &#38; Yan, S. (2015). Segmentation
    over detection via optimal sparse reconstructions. <i>IEEE Transactions on Circuits
    and Systems for Video Technology</i>. IEEE. <a href="https://doi.org/10.1109/TCSVT.2014.2379972">https://doi.org/10.1109/TCSVT.2014.2379972</a>
  chicago: Xia, Wei, Csaba Domokos, Junjun Xiong, Loongfah Cheong, and Shuicheng Yan.
    “Segmentation over Detection via Optimal Sparse Reconstructions.” <i>IEEE Transactions
    on Circuits and Systems for Video Technology</i>. IEEE, 2015. <a href="https://doi.org/10.1109/TCSVT.2014.2379972">https://doi.org/10.1109/TCSVT.2014.2379972</a>.
  ieee: W. Xia, C. Domokos, J. Xiong, L. Cheong, and S. Yan, “Segmentation over detection
    via optimal sparse reconstructions,” <i>IEEE Transactions on Circuits and Systems
    for Video Technology</i>, 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.”
    <i>IEEE Transactions on Circuits and Systems for Video Technology</i>, vol. 25,
    no. 8, IEEE, 2015, pp. 1295–308, doi:<a href="https://doi.org/10.1109/TCSVT.2014.2379972">10.1109/TCSVT.2014.2379972</a>.
  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'
...