Salient object detection via a novel saliency ranking tree integrating low-level and high-level features

Yu Pang; Yang Huang; Xiaosheng Yu; Chengdong Wu

doi:10.1117/12.3021395

25 March 2024 Salient object detection via a novel saliency ranking tree integrating low-level and high-level features

Yu Pang, Yang Huang, Xiaosheng Yu, Chengdong Wu

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130891X (2024) https://doi.org/10.1117/12.3021395
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

In this paper, we propose a novel salient object detection framework by constructing a novel saliency tree model integrating low-level and high-level features. In our model, numerous features containing low-level features (e.g., color, texture, gradient, contrast, etc.) and high-level features (e.g., deep features extracted from pre-trained VGG19 net) are firstly selected as candidate features. We develop a novel feature integrating mechanism to acquire an integrated feature descriptor which is more discriminative to capture the contrast between foreground and background for the input image. Then, we construct a novel saliency tree model relied on the integrated features to generate saliency map. We compare the proposed method and other state-of-the-art methods on three datasets, experimental results indicate that the proposed saliency detection algorithm has achieved the top performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Pang, Yang Huang, Xiaosheng Yu, and Chengdong Wu "Salient object detection via a novel saliency ranking tree integrating low-level and high-level features", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130891X (25 March 2024); https://doi.org/10.1117/12.3021395

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KEYWORDS

Object detection

Feature extraction

RGB color model

Education and training

Image segmentation

Mathematical optimization

Performance modeling

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