Abstract
Salient object detection has drawn much attention recently. The extracted salient objects can be used for tasks including recognition, segmentation and retrieval. Most existing models for saliency detection can only be applied to the high contrast and normal contrast scenes. They are not robust enough to handle objects in low contrast images. Due to low lightness, low Signal to Noise Ratio (SNR) and low appearance information, saliency detection in low contrast scenes remains a challenge. To solve this problem, this paper proposes a saliency detection model based on the feature extraction, in which optimal features have been learned in different contrast environment to improve the effectiveness of salient object detection. Compared with other existing saliency models, the proposed method has strong adaptability for different scenes, and gives superior performance in detecting the salient object especially in low contrast images.
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Acknowledgement
This work was supported by the Natural Science Foundation of Hubei Provincial of China (2014CFB247), and the National Natural Science Foundation of China (No. 61440016, 61273303).
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Mu, N., Xu, X., Li, Z. (2015). Hierarchical Features Fusion for Salient Object Detection in Low Contrast Images. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_29
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DOI: https://doi.org/10.1007/978-3-319-22186-1_29
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