Abstract
Salient object detection has been explored extensively in low dimensional images like RGB, grayscale, etc., but have been explored very little in high dimensional images like Hyperspectral images (HSI) etc. In HSI, few studies have used low-level features to perform salient object detection. In this paper, we propose a high-level feature-based salient object detection algorithm. The manifold ranking is applied on the self-supervised CNN features learned by an unsupervised segmentation task. The training of the model continues until the clustering loss or saliency map converges to a defined error. We found out that the proposed algorithm performed better than state-of-the-art in terms of precision.
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Notes
- 1.
We implemented the SUDF [6] and got the precision of 55.4% for the same dataset.
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Lone, Z.A., Pais, A.R. (2024). Salient Object Detection in Hyperspectral Images Using Felzenswalb’s Segmentation Algorithm. In: Ghosh, A., King, I., Bhattacharyya, M., Sankar Ray, S., K. Pal, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2021. Lecture Notes in Computer Science, vol 13102. Springer, Cham. https://doi.org/10.1007/978-3-031-12700-7_46
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