Semi-supervised saliency classifier based on a linear feedback control system model

Shuwei Huo, Yuan Zhou, Sun Yuan Kung

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

Abstract

Linear feedback control systems (LFCS) are amenable to numerous object recognition and detection tasks on account of its functional properties in signal filtering and error correction. In fact, there exists an intimate relationship between control states and salient values. Therefore, we propose a novel semi-supervised classifier which makes use of linear feedback control theory to improve saliency detection performance. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which may lead to the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using a LCSF model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. Based on our experiments on public datasets, it can be demonstrated that our approach significantly outperforms the state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3130-3137
Number of pages8
ISBN (Electronic)9781509061815
DOIs
StatePublished - Jun 30 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period5/14/175/19/17

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Huo, S., Zhou, Y., & Kung, S. Y. (2017). Semi-supervised saliency classifier based on a linear feedback control system model. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 3130-3137). [7966246] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966246