By constructing a two-dimensional (2D) compressive sensing (CS) measurement model

a new image retrieval algorithm is proposed by extracting hierarchical HSV features and texture features. Firstly

the ideas of grid discrete partition and hierarchical mapping in HSV space are introduced

and hierarchical mapping matrix and similar GLCM in HSV grid space are defined. Secondly

the normalized Gauss random matrix is designed as measurement matrix

and compressive sampling on the above two matrixes is performed by 2D CS measurement model. With using PCA（Principal Component Analysis）

the feature sequences as hierarchical HSV features and texture features are obtained from the above two hierarchical sampling matrixes. Finally

the above two features are infused to compute the overall similarity among images. Experimental results show that the above two features have good discrimination. It can improve the efficiency for image retrieval

and has good performance especially for images with complex backgrounds.