Effective infrared ship image segmentation using fuzzy correlation and graph cut optimization

Segmentation of infrared ship is an important step for maritime surveillance. Due to the low contrast of the image and fuzziness of gray level distribution, the high-precision maximum fuzzy correlation method can take place of existing maximum fuzzy entropy method to segment infrared ship, since it can measure the appropriateness of the fuzzy segmentation better. Nevertheless, the maximum fuzzy correlation segmentation is computationally expensive. Straightforward implementation of maximum fuzzy correlation segmentation on infrared ship images may obtain incomplete segmentation results, since spatial coherence is not enforced. For addressing that, the image enhancement technique based on fuzzy sure entropy is used before the segmentation to alleviate the low-contrast problem. Then the fuzzy correlation model with three-parameter membership function is used to segment infrared ship images. For reducing the computational cost, an iterative calculation strategy is proposed for eliminating the repeated computation. Finally, the probabilities of fuzzy events obtained from maximum fuzzy correlation are used to set the data terms of graph cut for getting the spatial coherent segmentation results. Quantitative evaluations over 80 low-contrast infrared ship images demonstrate that the proposed method could effectively segment infrared ship targets and outperform several existing segmentation methods in terms of precision and efficiency.