An image retrieval method, comprising: acquiring a query image; determining a target feature of the query image based on a pre-trained first deep neural network; wherein the first deep neural network is obtained by training according to sample images and predetermined features that are able to form the target feature and correspond to the sample images; obtaining a target feature of each of a plurality of images to be retrieved; calculating a similarity between the target feature of the query image and the target feature of each image to be retrieved; and determining a retrieval image corresponding to the query image from the plurality of images to be retrieved according to the calculated similarities, wherein the predetermined features are features of regions of interest, and the target feature is a feature aggregated with the features of the regions of interest; and wherein determining a target feature of the query image based on the pre-trained first deep neural network comprises: inputting the query image into the pre-trained first deep neural network to obtain a feature map, wherein the first deep neural network is obtained by training according to the sample images and regions of interest corresponding to the sample images, and the value of each position in the feature map represents the recognition capability of the original position of the correspondingly input query image; performing thresholding and morphological operations on the feature map to obtain a plurality of sub-regions with strong recognition capabilities; determining the plurality of sub-regions as target regions of interest of the query image; inputting the target region of interest into a pre-trained second deep neural network to obtain a target region-of-interest feature of the target region of interest, wherein the second deep neural network is obtained by training according to the regions of interest and region-of-interest features of the regions of interest; and aggregating the target region-of-interest features into the target feature of the query image. acquiring a query image; determining a target feature of the query image based on a pre-trained first deep neural network; wherein the first deep neural network is obtained by training according to sample images and predetermined features that are able to form the target feature and correspond to the sample images; obtaining a target feature of each of a plurality of images to be retrieved; calculating a similarity between the target feature of the query image and the target feature of each image to be retrieved; and determining a retrieval image corresponding to the query image from the plurality of images to be retrieved according to the calculated similarities, wherein the predetermined features are features of regions of interest, and the target feature is a feature aggregated with the features of the regions of interest; and wherein determining a target feature of the query image based on the pre-trained first deep neural network comprises: inputting the query image into the pre-trained first deep neural network to obtain a feature map, wherein the first deep neural network is obtained by training according to the sample images and regions of interest corresponding to the sample images, and the value of each position in the feature map represents the recognition capability of the original position of the correspondingly input query image; performing thresholding and morphological operations on the feature map to obtain a plurality of sub-regions with strong recognition capabilities; determining the plurality of sub-regions as target regions of interest of the query image; inputting the target region of interest into a pre-trained second deep neural network to obtain a target region-of-interest feature of the target region of interest, wherein the second deep neural network is obtained by training according to the regions of interest and region-of-interest features of the regions of interest; and aggregating the target region-of-interest features into the target feature of the query image. inputting the query image into the pre-trained first deep neural network to obtain a feature map, wherein the first deep neural network is obtained by training according to the sample images and regions of interest corresponding to the sample images, and the value of each position in the feature map represents the recognition capability of the original position of the correspondingly input query image; performing thresholding and morphological operations on the feature map to obtain a plurality of sub-regions with strong recognition capabilities; determining the plurality of sub-regions as target regions of interest of the query image; inputting the target region of interest into a pre-trained second deep neural network to obtain a target region-of-interest feature of the target region of interest, wherein the second deep neural network is obtained by training according to the regions of interest and region-of-interest features of the regions of interest; and aggregating the target region-of-interest features into the target feature of the query image.