Abstract
Saliency detection plays an important role in computer vision field. In this paper, a saliency detection model for images is proposed based on frequency domain analysis and spatial information. Saliency is detected in the proposed model via the following main steps: coefficients of the input hyper complex image are designed in Hyper Complex Fourier Transform (HFT); then, during the analysis of the spectrum scale space in frequency domain, a series of saliency maps at different scales are obtained; finally, considering spatial relationship, the spatial contrast function is taken for selecting the optimal saliency map. Experimental results show that the proposed model can achieve high performance in terms of the average AUC and F-measure evaluation metrics and outperform state-of-the-art baselines.
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