skip to main content
10.1145/2632856.2632945acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicimcsConference Proceedingsconference-collections
research-article

How Important is Location in Saliency Detection

Published: 10 July 2014 Publication History

Abstract

Current saliency detection methods mainly work on exploring the potential of low-level and high-level visual features, such as color, texture and face, but treat location information as a weak assistance or completely ignore it. In this paper, we reveal the importance of location information in saliency detection. We analyze the largest public image dataset for saliency detection THUS10000, and and the relationship between content location and saliency distribution. To further validate the effect of location information, we propose two location based saliency detection approaches, location based Gaussian distribution and location based saliency propagation, which make use of no or weak assistance of image content. Experimental results show that location based saliency detection can obtain much better performance than random selection, even better than most state-of-the-art saliency detection methods.

References

[1]
R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, pages 1597--1604. IEEE, 2009.
[2]
S. Alpert, M. Galun, R. Basri, and A. Brandt. Image segmentation by probabilistic bottom-up aggregation and cue integration. In CVPR, pages 1--8. IEEE, 2007.
[3]
A. Borji, H. R. Tavakoli, D. N. Sihite, and L. Itti. Analysis of scores, datasets, and models in visual saliency prediction. In ICCV, pages 921--928. IEEE, 2013.
[4]
M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu. Global contrast based salient region detection. In CVPR, pages 409--416. IEEE, 2011.
[5]
L. Itti, C. Koch, E. Niebur, et al. A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI, 20(11):1254--1259, 1998.
[6]
Y. Jia and M. Han. Category-independent object-level saliency detection. In ICCV, pages 1761--1768. IEEE, 2013.
[7]
P. Jiang, H. Ling, J. Yu, and J. Peng. Salient region detection by ufo: Uniqueness, focusness and objectness. In ICCV, pages 1976--1983. IEEE, 2013.
[8]
T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In ICCV, pages 2106--2113. IEEE, 2009.
[9]
T. Liu, S. D. Slotnick, J. T. Serences, and S. Yantis. Cortical mechanisms of feature-based attentional control. Cerebral Cortex, 13(12):1334--1343, 2003.
[10]
T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. IEEE TPAMI, 33(2):353--367, 2011.
[11]
R. Margolin, A. Tal, and L. Zelnik-Manor. What makes a patch distinct? In CVPR, pages 1139--1146. IEEE, 2013.
[12]
V. Movahedi and J. H. Elder. Design and perceptual validation of performance measures for salient object segmentation. In CVPR Workshops, pages 49--56. IEEE, 2010.
[13]
X. Yang, T. Zhang, and C. Xu. Locality discriminative coding for image classification. In ICIMCS, pages 52--55. ACM, 2013.
[14]
S. Zhang, Q. Tian, Q. Huang, W. Gao, and Y. Rui. Multi-order visual phrase for scalable image search. In ICIMCS, pages 145--149. ACM, 2013.

Cited By

View all
  • (2015)Salient object detection in RGB-D image based on saliency fusion and propagationProceedings of the 7th International Conference on Internet Multimedia Computing and Service10.1145/2808492.2808551(1-5)Online publication date: 19-Aug-2015
  • (2015)Flat3D: Browsing Stereo Images on a Conventional ScreenMultiMedia Modeling10.1007/978-3-319-14445-0_47(546-558)Online publication date: 2015
  • (2014)Depth saliency based on anisotropic center-surround difference2014 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2014.7025222(1115-1119)Online publication date: Oct-2014

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • NSF of China: National Natural Science Foundation of China
  • Beijing ACM SIGMM Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Saliency detection
  2. location information
  3. patch representation
  4. saliency propagation

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICIMCS '14

Acceptance Rates

Overall Acceptance Rate 163 of 456 submissions, 36%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2015)Salient object detection in RGB-D image based on saliency fusion and propagationProceedings of the 7th International Conference on Internet Multimedia Computing and Service10.1145/2808492.2808551(1-5)Online publication date: 19-Aug-2015
  • (2015)Flat3D: Browsing Stereo Images on a Conventional ScreenMultiMedia Modeling10.1007/978-3-319-14445-0_47(546-558)Online publication date: 2015
  • (2014)Depth saliency based on anisotropic center-surround difference2014 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2014.7025222(1115-1119)Online publication date: Oct-2014

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media