基于二维压缩感知和分层特征的图像检索算法

摘要

To retain the position relationship of pixels when image analyzing and dimension reducing,we extend the one-dimensional compressive sensing theory to two-dimensional,and establish a two-dimensional compressive measurement model for sparse signal.We study an adaptive gradient descent recursion algorithm for two-dimensional signal,and propose an image hierarchical feature extraction and retrieval method.Firstly,it conducts grid discrete division on the RGB color space,and mapping to the image by hierarchical operator.It defines an extended GLCM based on color grid space,and ex-tracts the hierarchical measurement feature,texture feature and hierarchical color statistical feature by the two-dimensional measurement model.The hierarchical measurement feature of image reflects the position relationship between the image color and pixel,and the extended GLCM reflects the texture feature.Secondly,it calculates the original signal difference and sparse value between images by the AGDR algorithm.Finally,it calculates the overall similarity metrics between images by combi-ning the two hierarchical feature difference,the sparse value and the color statistical feature.The simulation results show that the image retrieval method which applying hierarchical two-dimensional compressive sensing measurement and AGDR algo-rithm has superior performance on retrieval time,recall and precision,it provides a new idea for the image retrieval.%为了保留图像分析时的像素点位置关系及降维处理，把一维压缩感知理论推广到二维，建立了二维可稀疏信号的压缩测量模型，研究了一种二维信号的自适应梯度下降重构AGDR（Adaptive Gradient Descent Recursion）算法，由此提出了一种图像分层特征提取与检索方法。首先对图像在RGB颜色空间上进行网格离散划分，通过分层算子对图像进行分层映射，定义一种基于颜色网格空间的扩展灰度共生矩阵，采用二维测量模型获取图像的分层测量特征、纹理特征与分层颜色统计特征，图像分层测量特征综合反映出图像的颜色及像素点位置的关系，扩展灰度共生矩阵反映纹理特征。其次用AGDR算法计算检索图像之间的原始信号差量及其稀疏值。最后结合两类分层特征差量、稀疏值和颜色统计特征，融合计算图像间整体相似度度量指标。仿真实验表明，应用分层二维压缩感知测量与AGDR算法的图像检索方法在检索时间、查全率和查准率等指标上具有优越性能，为图像检索提供了新思路。