A camera acquires an image of an item involved in an interaction, such as the item being picked or placed on a shelf by a user. The item depicted in the image is identified via item recognition using a customized neural network that uses previously trained subnetworks for a set of candidate items. The candidate items are determined based on items near a user location, items associated with a user cart, items in a shopping list or wish list associated with the user, or items otherwise associated with the user. Each previously trained subnetwork for a candidate item includes one or more reference images and corresponding reference feature data. The customized neural network uses a matching and inference network to test the image against the candidate items and provide comparison data indicative of a similarity between the item depicted in the image and one of the candidate items.

A method of establishing an image set for image recognition includes obtaining a single-label image set comprising an image annotated with a single label, and a multi-label image set comprising an image annotated with a plurality of labels; converting content of each label into a corresponding word identifier according to a semantic network, to obtain a word identifier set, a converted single-label image set, and a converted multi-label image set; and constructing a hierarchical semantic structure according to the word identifier set and the semantic network. The method also includes performing label supplementation on the image in the converted single-label image set to obtain a supplemented single-label image set; performing label supplementation on the supplemented single-label image set to obtain a final supplemented image set; and establishing a target multi-label image set to train an image recognition model by using the target multi-label image set.

A method of establishing an image set for image recognition includes obtaining a single-label image set comprising an image annotated with a single label, and a multi-label image set comprising an image annotated with a plurality of labels; converting content of each label into a corresponding word identifier according to a semantic network, to obtain a word identifier set, a converted single-label image set, and a converted multi-label image set; and constructing a hierarchical semantic structure according to the word identifier set and the semantic network. The method also includes performing label supplementation on the image in the converted single-label image set to obtain a supplemented single-label image set; performing label supplementation on the supplemented single-label image set to obtain a final supplemented image set; and establishing a target multi-label image set to train an image recognition model by using the target multi-label image set.

The disclosure relates to an artificial intelligence (AI) system using a machine learning algorithm such as deep learning and the like, and an application thereof. In particular, there is provided a control method for an electronic apparatus for searching for an image, the method comprising displaying an image comprising at least one object, detecting a user input for selecting an object, recognizing an object displayed at a point at which the user input is detected and acquiring information regarding the recognized object by using a recognition model trained to acquire information regarding an object, displaying a list including the information regarding the object, and based on one piece of information being selected from the information regarding the object included in the list, providing a related image by searching for the related image based on the selected information.

Apparatus for performing searching of a plurality of reference images, the apparatus including one or more electronic processing devices that search the plurality of reference images to identify first reference images similar to a sample image, identify image tags associated with at least one of the first reference image, search the plurality of reference images to identify second reference images using at least one of the image tags and provide search results including at least some first and second reference images.

Apparatus for performing searching of a plurality of reference images, the apparatus including one or more electronic processing devices that search the plurality of reference images to identify first reference images similar to a sample image, identify image tags associated with at least one of the first reference image, search the plurality of reference images to identify second reference images using at least one of the image tags and provide search results including at least some first and second reference images.

A digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from a user's social media account or her or his community members' social media accounts. The integrated camera is used to automatically determine an identity of a frame viewer, and a viewer profile automatically determined from the identity of the viewer. The displayed photos are automatically shown and/or changed according to the detected viewers.

A digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from a user's social media account or her or his community members' social media accounts. The integrated camera is used to automatically determine an identity of a frame viewer, and a viewer profile automatically determined from the identity of the viewer. The displayed photos are automatically shown and/or changed according to the detected viewers.

A method of establishing an image set for image recognition includes obtaining a single-label image set comprising an image annotated with a single label, and a multi-label image set comprising an image annotated with a plurality of labels; converting content of each label into a corresponding word identifier according to a semantic network, to obtain a word identifier set, a converted single-label image set, and a converted multi-label image set; and constructing a hierarchical semantic structure according to the word identifier set and the semantic network. The method also includes performing label supplementation on the image in the converted single-label image set to obtain a supplemented single-label image set; performing label supplementation on the supplemented single-label image set to obtain a final supplemented image set; and establishing a target multi-label image set to train an image recognition model by using the target multi-label image set.

Implementations are described for determining, based on a user-assembled collection of electronic content items, at least one actual purpose for the assembled collection. Those implementations are further directed to selecting an additional electronic content item based on the at least one actual purpose being assigned to the additional electronic content item in an electronic database. Those implementations are yet further directed to, responsive to the selecting, causing a suggestion for the additional electronic content item to be rendered at a client device associated with a user account that is assigned to the user-assembled collection.