基于局部复杂度和初级视觉特征的自底向上注意信息提取算法  

Extracting Bottom-Up Attention Information Based on Local Complexity and Early Visual Features

作　　者：田媚[1] 罗四维[1] 黄雅平[1] 赵嘉莉[1] 

机构地区：[1]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机研究与发展》2008年第10期1739-1746,共8页Journal of Computer Research and Development

基　　金：国家"八六三"高技术研究发展计划基金项目(2007AA01Z168);国家自然科学基金项目(60773016);教育部博士点基金项目(20050004001);北京交通大学科技基金项目(2008RC008)~~

摘　　要：借鉴心理学中有关视觉注意的研究成果,提出了一种新的自底向上的注意信息提取算法.自底向上的注意信息由图像中每个点对应区域的显著性构成,区域的尺度自适应于局部特征的复杂度.新的显著性度量标准综合考虑了局部复杂度、统计不相似和初级视觉特征这3个方面的特性.显著区域在特征空间和尺度空间中同时显著.获取的自底向上的注意信息具有旋转、平移、比例缩放不变性和一定的抗噪能力.以该算法为核心,构建了一个注意模型,将其应用于多幅自然图像的实验证明了算法的有效性.During the last few years, extraordinary progress has been made in understanding the basic principle of how information is processed by visual cortex. This brings more and more concerning to bottom-up attention at home and abroad, and a large number of models based on it are built. But difficulties are met about how to extract efficient bottom-up attention information which is invariant to image scale, rotation and translation. Inspired by the research of visual attention in psychology, a novel algorithm for extracting bottom-up attention information （integration of local complexity and early visual features, LOCEV） is proposed in this paper. Bottom-up attention information is composed by saliency of certain regions correspond to each point in image, and scale of the region varies with complexity of local features adaptively. New saliency metric is defined as a product of three terms. local complexity, statistical dissimilarity and early visual features. Saliency of certain regions corresponding to all points in image is defined as bottom-up attention information. Salient regions are salient both in feature space and over scale. The extracted bottom-up attention information is invariant to image scale, rotation and translation, and is shown to be robust to noise. An attention model is developed based on this algorithm. Experiments results with natural images demonstrate its effectiveness.

关 键 词：视觉注意 视觉显著性 显著区域 局部复杂度 初级视觉特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]