Salient object detection via multi-spectral co-connectivity and collaborative graph ranking

Xin Wang; Xia Wang; Xin Zhang

doi:10.1117/12.2575724

5 November 2020 Salient object detection via multi-spectral co-connectivity and collaborative graph ranking

Xin Wang, Xia Wang, Xin Zhang

Proceedings Volume 11563, AOPC 2020: Infrared Device and Infrared Technology; 1156303 (2020) https://doi.org/10.1117/12.2575724
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

Salient object detection (SOD) has become an active research direction with extensive applications in computer vision tasks. Although integrating RGB and infrared thermal (RGB-T) data has proven to be effective in adverse environments, it is difficult for RGB-T SOD methods to highlight the salient objects completely when objects cross the image boundary. To address the aforementioned problem, this paper proposes an effective RGB-T SOD algorithm based on multi-spectral co-connectivity (MSCC) and collaborative graph ranking. Specifically, we introduce the multi-spectral weighted color distance to construct an improved undirected weighted graph and compute the MSCC-based saliency map. Simultaneously, the MSCC-based background probability map is also calculated and employed in the following processing of real background seeds selection. Then, we utilize collaborative graph learning (CGL) and calculate the CGL-based saliency map in a two-stage ranking framework. Finally, we integrate these two saliency maps through multiplying or averaging to enhance the final saliency result. The experimental comparison results of 5 quantitative evaluation indicators between the proposed algorithm and 9 state-of-the-art methods on RGB-thermal datasets VT821 and VT1000 datasets demonstrate the robustness and superiority of the proposed work.

Citation Download Citation

Xin Wang, Xia Wang, and Xin Zhang "Salient object detection via multi-spectral co-connectivity and collaborative graph ranking", Proc. SPIE 11563, AOPC 2020: Infrared Device and Infrared Technology, 1156303 (5 November 2020); https://doi.org/10.1117/12.2575724

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KEYWORDS

RGB color model

Infrared radiation

Thermography

Control systems

Image segmentation

Infrared imaging

Visible radiation

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