A method, performed by a device, of identifying an object in an image by using a neural network, the neural network comprising a learning network model and at least one feature extraction layer, the method comprising: obtaining an image comprising an object; obtaining attribute information of the image by using a plurality of layers included in the learning network model for determining a category of the object; obtaining feature information representing the object by inputting attribute information obtained from at least some of the plurality of layers of the learning network model to the at least one feature extraction layer, wherein the feature information comprises a feature vector representing the object included in the image; and determining a distance between the obtained feature information and feature information of each of a plurality of stored images including at least one object; identifying an image among the stored images based on the determined distance, and identifying the object in the image as an object in the identified image; characterized in that: a criterion used for selecting the at least some of the plurality of layers is determined through a training result based on a database comprising a plurality of images, wherein the database comprises a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity, wherein determining the criterion comprises: obtaining a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster; and training the at least one feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value. obtaining an image comprising an object; obtaining attribute information of the image by using a plurality of layers included in the learning network model for determining a category of the object; obtaining feature information representing the object by inputting attribute information obtained from at least some of the plurality of layers of the learning network model to the at least one feature extraction layer, wherein the feature information comprises a feature vector representing the object included in the image; and determining a distance between the obtained feature information and feature information of each of a plurality of stored images including at least one object; identifying an image among the stored images based on the determined distance, and identifying the object in the image as an object in the identified image; characterized in that: a criterion used for selecting the at least some of the plurality of layers is determined through a training result based on a database comprising a plurality of images, wherein the database comprises a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity, wherein determining the criterion comprises: obtaining a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster; and training the at least one feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value. a criterion used for selecting the at least some of the plurality of layers is determined through a training result based on a database comprising a plurality of images, wherein the database comprises a first similarity cluster and a second similarity cluster which are generated as a result of classifying the plurality of images according to similarity, wherein determining the criterion comprises: obtaining a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster; and training the at least one feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value. obtaining a first image and a second image that are included in the first similarity cluster and a third image included in the second similarity cluster; and training the at least one feature extraction layer such that a difference between feature information of the first image and feature information of the second image is equal to or less than a first threshold value and a difference between the feature information of the first image and feature information of the third image is equal to or greater than a second threshold value.