Salient object detection via a linear feedback control system

Shuwei Huo, Yuan Zhou, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Linear feedback control systems (LFCS) have been widely applied in signal analysis, filtering, and error correction. Many functional properties of LFCS are amenable to numerous object recognition and detection tasks. In fact, there exists an intimate relationship between control states and salient values. This prompts us to adopt the linear feedback control system to detect salient object in static images. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. In addition, to initialize the system, we propose the so called boundary homogeneity based on a priori knowledge on the boundary to estimate the background likelihood and indirectly depict a foreground (saliency) map. By our experimental results, we demonstrates that such feedback control model can bring about noticable improvement in salient object detection.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4257-4261
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - Jul 2 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period9/17/179/20/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Iterative optimization
  • Linear feedback control system
  • Saliency detection

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