For solving the problems of the complexity about feature extraction on largesize image and the loss of feature information

the characteristics of compressive sensing (CS) theory that a small amount of measurements can accurately reconstruct the original signal is used

and a new image retrieval method based on compressive sensing is proposed. First

the wavelet transformation is performed

and the image is divided into blocks. Then

blocked polynomial deterministic measurement matrix and conduct fast CS measurement on blocked image constructed

and very few compressive measurements which represent the image features can be obtained. Finally

we calculate the similarity between the measurement features by weight distance is calculated

so to implement accurate image retrieval. At the same time

it is proved theoretically that the blocked polynomial deterministic measurement matrix is satisfied to the restricted isometric property (RIP). Experimental results show that this method has higher performance on image precision and image recall

and can be applied to massive complex image retrieval.