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Cosaliency Detection in Images Using Structured Matrix Decomposition and Objectness Prior

  • Sayanti BardhanEmail author
  • Shibu Jacob
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Cosaliency detection methods typically fail to perform in the situation where the foreground has multiple components. This paper proposes a novel framework, Cosaliency via Structured Matrix Decomposition (CSMD), for efficient detection of cosalient objects. This paper addresses the issue of saliency detection for images with multiple components in the foreground, by proposing a superpixel level, objectness prior. In this paper, we further propose a novel fusion technique to combine objectness prior to the cosaliency object detection framework. The proposed model is evaluated on challenging benchmark cosaliency dataset that has multiple components in the foreground. It outperforms prominent state-of-the-art methods in terms of efficiency.

Keywords

Cosaliency detection Matrix decomposition Fusion Objectness 

Notes

Acknowledgements

The authors thank the faculty and members of Visualization and Perception Lab, Department of Computer Science and Engineering, IIT-Madras, for the constant guidance and support for this work. The authors would also like to acknowledge Marine Sensor Systems Lab, NIOT and Director, NIOT for their support.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Visualization and Perception Lab, Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia
  2. 2.Marine Sensor SystemsNational Institute of Ocean TechnologyChennaiIndia

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