基于聚类分割和特征点的显著区域检测算法

针对彩色图像中的显著区域检测，对基于聚类分割的特征点检测算法及基于亮度、颜色和梯度多特征的显著区域检测算法进行了研究，提出一种基于特征点和聚类分割的显著区域检测算法，该算法的处理思路是先对目标彩色图像利用高斯低通滤波和局部熵纹理分割去除纹理区，得到R、G、B分量的滤波灰度图，聚类分割自动划分出每个分量的最亮区域、最暗区域和剩余区域这三个区域，每个颜色分量选择最亮或最暗这两个区域与剩余区域亮度差值最大的一个区域，对此选择区域边界进行角点、边缘点检测，将其角点和边缘点作为显著点，然后通过数学形态学将显著点扩展到显著区域.利用公共数据库中的多幅自然图像进行实验对比，实验结果显示本文所提算法不仅提高了检测准确性，同时简化了计算过程，验证了该算法在提取尤其是纹理复杂的图像的显著区域上的有效性.

New Regions of Interest Detector Algorithm Based on Clustering Segmentation and Feature Points

This study proposes a saliency detection algorithm based on the fuzzy enhancement and feature points, using fuzzy enhancement and clustering segmentation to highlights the image object and internal classification. First, extract significant edge points and corner points, calculate the multiple features'' means of those points, such as the brightness, color, and gradient features. Then, find all points which are belong to salient regions are closer to the means in the original image. By mathematical morphology to make sure the largest connected region, get salient regions finally. The experimental results show that the algorithm presented in this paper for saliency detection, can improve the accuracy and simplify the computation, the algorithm has an important role in the saliency detection, especially complex texture image.