视觉选择性注意模型化计算中的特征整合权值估计与图像显著性区域提取  

Weight estimation for feature integration and saliency region extraction in modeling computation of visual selective attention

作　　者：刘琼[1] 秦世引[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《模式识别与人工智能》2011年第4期548-554,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目资助(No.60875072)

摘　　要：针对视觉选择性注意模型化计算过程中不同特征在整合阶段的权值判定,提出一种基于特征图分布的权值估计方法,并在静态图像显著性区域提取中取得了令人满意的应用效果.首先提取原始图像的颜色、方向和强度特征图像,然后计算各个特征图的广义高斯分布参数与方差,进而给出一种特征图权值估计算法,最后通过对特征图的加权整合与归一化实现对原始图像的显著性区域提取.实验结果表明,通过此方法计算的权值对特征进行加权调制所提取的显著性区域的效果更加符合人眼的观测结果.According to the different importance of features during the feature integration process in modeling computation of visual selective attention, a method of weight estimation for different features is presented to highlight the acceptable saliency region of image in the bottom-up computational model. Firstly, the color, orientation and intensity features are extracted by mimicking the function of feature sensitive neurons of human primaI~~ visual cortex. Then the importance of each feature is estimated according to the generalized Gaussian distribution and the variance of its feature map. Finally, the saliency regions are extracted by weighted integration and normalization. The experimental results demonstrate that the proposed method outperforms traditional methods to meet the requirement of observers.

关 键 词：视觉选择性注意模型化计算 权值估计 特征图提取 特征整合 显著性区域提取 

分 类 号：B842[哲学宗教—基础心理学]