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 extracts 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 combining 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 algorithm has superior performance on retrieval time

recall and precision

it provides a new idea for the image retrieval.