基于HSV空间改进的多尺度显著性检测

图像显著性特征已被广泛地应用于图像分割、图像检索和图像压缩等领域,针对传统算法耗时较长,易受噪声影响等问题,提出了一种基于HSV色彩空间改进的多尺度显著性检测方法。该方法选择HSV色彩空间的色调、饱和度和亮度作为视觉特征,先通过高斯金字塔分解获得三种尺度的图像序列,然后使用改进的SR算法从三种尺度的图像序列中提出每个特征图,最后将这些特征图进行点对点的平方融合和线性融合。与其它算法的对比实验表明,该方法具有较好的检测效果和鲁棒性,能够较快速地检测出图像的显著性区域,能够突显整个显著性目标。

Improved multi-scale saliency detection based on HSV space

Image saliency has been widely used in image segmentation, image retrieval, and image compression and so on. In order to solve the problems of the traditional algorithms, such as huge time consumption and sensitive to noise, we propose an improved multi-scale saliency detection based on hue, saturation, value (HSV) color space. The method chooses the hue, saturation and brightness of HSV color space as visual features. Firstly, we obtain the three-scale image sequences via the Gauss pyramid decomposition. And then, we extract each feature map from the three-scale sequence images through the improved SR algorithm. Finally, these feature maps are fused point to point by square operation and liner operation. Experiments show that, compared with the existing methods, the proposed method has better detecting effect and robustness, and it can quickly detect the saliency region of the image and highlight the entire salient object.