A visual attention model based on wavelet transform and its application on ship detection

Human visual system is very efficient and selective in scene analysis, which has been widely used in image processing. In this paper, a new visual attention model based on dyadic wavelet transform (DWT) used for ship detection is proposed. It is a bottom-up visual attention model driven by data rather than by task. First, the input image is converted from RGB color space to HIS color space. Second, the modulus of DWT is analyzed to obtain the conspicuity map of each feature. Third, the conspicuity maps are combined into the saliency map nonlinearly, different from Itti's method, the contribution rate of each conspicuity map to final saliency map is not equal. It is relevant to the difference between the level of the most active region and the average level of the other active regions in each conspicuity map. Finally, the detection result of ships based on saliency map is got by region growing method, where the seed is obtained from the saliency map and the growing process is implemented in intensity image. Experiments on natural ship images show that our method is robust and efficient compared with Itti's and Hou's method.

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