基于自适应半径搜索的图像感兴趣区域检测

Detection of Interest Image Region Based on Adaptive Radius Search

感兴趣区域是图像中重要性较高并被优先关注的部分。传统视觉关注模型利用半径固定的圆描述感兴趣区轮廓,无法获得感兴趣区域的精确描述。提出一种基于自适应半径搜索的图像感兴趣区自动检测方法。提取图像的颜色、亮度和方向特征并生成多尺度的视觉显著图;通过计算显著图的全局显著度阈值获得视觉注意焦点搜索结束的条件;利用基于显著比的自适应半径搜索策略获取感兴趣区的精确描述信息。实验结果表明,新方法不仅能够提高对图像感兴趣区的自动检测精度,而且更符合人眼视觉系统的特点。对今后基于图像感兴趣区的目标自动识别具有重要价值。

The regions of an interest image are the parts of priority attention and have more significance. The traditional visual attention model describes the information of regions of interest using fixed-size circles and can′t accurately express the outline of the regions of interest. A new automatic detection algorithm of the regions of interest based on adaptive radius search （ARS） is proposed. The new algorithm extracts color, intensity and orientation features of the image to generate a multi-scale saliency map. The global saliency threshold is calculated, which can get the end condition of searching the focus of attention. The adaptive radius search mechanism based on saliency ratio is proposed in the description of regions of interest to acquire the accurate information of regions of interest. The experimental results show that the new algorithm not only can effectively improve the detection precision of regions of interest, but also is more suitable to the features of human visual system. It has important value for the automatic target recognition of regions of interest in the future.