结合区域和边界信息的图像显著度检测

目的 图像显著度检测是许多图像应用的核心问题,为了能够在复杂背景下准确提取图像中前景对象的位置和尺度信息,提出一种结合区域和边界信息的图像显著度检测方法。方法 对于图像区域信息,提出一种基于图像等照度线的方法检测显著区域信息。该方法针对不同的特征(颜色、亮度和方向)提出统一的计算方法,使得不同特征下获得的显著信息具有一致的度量标准,从而方便后续多特征显著度图的融合。对于图像边界信息,采用一种结合多尺度Beltrami过滤器的全局方法检测显著边界信息。多尺度Beltrami过滤器可以显著增强图像中的边界信息。利用全局显著度检测方法对经过过滤器处理过的图像可以准确地获取图像中最为显著的边界信息。最后,由于区域和边界分别代表图像中的不同类型信息,可以直接采用线性融合方式构建最终的图像显著度图。结果 与其他9种流行图像显著度检测算法相比,本文算法无论在简单还是复杂背景下均能够较为准确地检测出图像中的显著度信息(Precision、Recall、F测试中获得的平均值为0.5905,0.6554,0.7470的最高测试结果)。结论 提出一种结合区域和边界信息的图像显著度检测算法,通过区域和边界信息相结合的方式实现图像中显著对象的准确检测。实验结果表明本文算法具有良好的适用性和鲁棒性,为图像中复杂背景下对象检测打下坚实基础。

Image saliency by regions and edges

Objective Computing salient information within digital images has been the focus of considerable research interest and has been applied in many applications, e.g., object detection, image segmentation, visual tracking, and content-based image retrieval. In most models, image saliency has been regarded as local regions that can be easily differentiated from their surroundings. However, the main problem existing in the models is that salient regions have limited ability to locate a complete object. A method that combines regions and edges is proposed to solve the problem. Method For regions, an isophote-based operator is designed to detect potential structures. According to the findings from neurobiology and psychophysics, salient information can be defined as regions that popped out from their surroundings based on certain feature channels, such as color, intensity, and orientation. Thus, the isophote-based operator is employed to extract salient regions from three kinds of features, namely, color, intensity, and orientation. The operator mainly establishes a consistent measurement for various feature channels, which easily integrates multi-feature saliency. For edges, a global saliency detector is adopted with the multi-scale Beltrami filter. The multi-scale Beltrami filter could enhance edge information within images while blurring detailed information of interior regions. By processing through the multi-scale Beltrami filter, global image saliency detectors could locate salient edges easily. Finally, the salient regions and edges are integrated by a linear method directly. Result The database used in this study includes 1 000 images from a variety of sources and has ground truths in the form of accurate human-marked labels for saliency information. Two kinds of measurements are adopted in the experiment, namely, segmentation by fixed thresholding and segmentation by adaptive thresholding. In both measurements, the proposed method exhibits impressive performance compared with nine other well-known methods, with 0.92 rate of receiver operating characteristic area in segmentation by adaptive thresholding and 0.5905, 0.6554, 0.7470 rate of average precision, recall, F-measure in segmentation by adaptive thresholding. Conclusion Image saliency is one of the key features for many applications. This study proposes a novel method that combines region and edge information to locate complete salient objects. As shown in the experiment, the proposed method has good applicability and robustness.