一种基于蚁群优化的显著边缘检测算法
doi: 10.3724/SP.J.1146.2013.01506
A Novel Salient Image Edge Detection Algorithm Based on Ant Colony Optimization
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摘要: 该文提出一种基于蚁群优化的显著边缘检测算法。该算法利用相位编组方法计算支持区面积作为描述图像边缘梯度方向一致性的指标,将梯度幅度和支持区面积结合起来形成启发信息和信息素增量的计算方法,采用线性加权方法将信息素、梯度幅度、支持区面积3种信息综合起来得到蚂蚁转移概率,通过引入禁忌表增大蚂蚁的活动范围。实验结果表明:该文提出的算法能够有效检测图像中的显著边缘特征,对多类图像都有良好的适应性,而且收敛速度较快。Abstract: In this paper, a novel salient image edge detection technique that is based on Ant Colony Optimization (ACO) is presented. Firstly, the proposed method designs a new edge saliency description called Support Region Area (SRA) using phase grouping algorithm. Then, two kind of heuristic information, SRA and gradient magnitude, are introduced in ACO to guide the ants movement. The quantity of pheromone laid by each ant on its new arrived node is calculated based the SRA and the gradient magnitude on the node. Each ants transition probability is calculated by a new method which linear weighted combines the pheromone, the gradient magnitude and the SRA in the ants 8-connectivity neighborhood. A taboo table is created for each ant that recorder the nodes it has recently visited, and is used to present the ant form visiting the same set of nodes repeatedly. Experimental results show the success of the technique in extracting salient edges from visual and infrared images.
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Key words:
- Image processing /
- Ant colony /
- Edge detection /
- Salient edge /
- Gradient orientation consistency
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