一种背景抑制改进的显著性目标检测方法  

A salient objects detection method based on background suppression improvement

作　　者：崔丽群[1] 赵越[1] 吴晓冬[1] 魏可飞 刘晨 CUI Li qun;ZHAO Yue;WU Xiao dong;WEI Ke fei;LIU Chen(School of Software,Liaoning Technical University,Huludao 125000,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125000

出　　处：《计算机工程与科学》2018年第8期1435-1443,共9页Computer Engineering & Science

基　　金：国家自然科学基金(61172144);辽宁省教育厅项目(L2012113)

摘　　要：针对显著性目标检测在复杂背景下准确率低的问题,提出超复数傅里叶变换改进的条件随机场显著性目标检测方法。首先,建立图像无向图并提取节点特征;然后重构超复数傅里叶变换得到平滑振幅谱与相位谱,获得无向图节点背景抑制权值,从而初步确定多尺度高斯核背景抑制图;最后输入到训练后的条件随机场中,通过增强目标表示得到最终显著性目标区域。实验表明,本文方法在准确率上较现有流行方法有显著提高,且能够在抑制复杂背景的同时,准确锁定指定目标位置区域。实验验证本文方法在复杂背景下显著性目标检测具有较好的准确性和鲁棒性。Aiming at the problem that the salient objects detection has low accuracy under complex background,an improved Conditional Random Field（CRF）salient objects detection method is proposed by using Hypercomplex Fourier Transformation（HFT）.Firstly,this method builds unoriented graphs and extracts the node features on the image;Then,the HFT is reconstructed to obtain the smooth amplitude spectrum and phase spectrum,and the background suppression weights of unoriented graph nodes are obtained.Thus,the multi-scale Gaussian kernel background suppression graphs are preliminarily determined.Finally,they are inputted into the trained Conditional Random Field,the final significant target area is obtained by enhancing target representation.Experimental results show that the proposed method has obviously higher accuracy than the existing methods and can restrain the complex background and lock the specified target location region accurately at the same time.Experiments verify that salient objects detection has good accuracy and robustness under complex background.

关 键 词：显著性目标检测 超复数傅里叶变换 背景抑制 条件随机场 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]