In the image processing device, the image processing method, the program, and the recording medium according to an embodiment of the present invention, a processor connected to a memory, the processor configured to receive an input of an image set owned by a user, analyze each image included in the image set, determine a plurality of tag information of an imaging content in the image set based on an analyzing result of each image, set one or more objectives to be achieved by the user based on the plurality of tag information, and set one or more items to be executed by the user for each of the one or more objectives based on the analyzing result of each image, and perform control such that at least one of the one or more objectives or the one or more items is displayed on a display.

The present disclosure relates to systems, methods, and non-transitory computer readable media for clustering media items in a semantic space to generate theme-based folders that organize media items by content theme. In particular, the disclosed systems can access media items that are stored in an original folder structure. The disclosed systems can generate content-based tags for each media item in a collection of media items. Based on the generated tags, the disclosed systems can map the collection of media items to a semantic space and cluster the collection of media items. The disclosed systems determine themes for the clusters based on the generated tags. The disclosed systems can present a media item navigation graphical user interface comprising the collection of media items organized by themes. The disclosed system can present the media item navigation graphical user interface without altering the original folder structure.

Provided are a robot positioning method and apparatus, an intelligent robot, and a storage medium. The method includes: configuring a camera and various sensors on a robot so that the robot may acquire an image collected by the camera and various sensing data collected by the various sensors (step 101); next extracting semantic information contained in the collected image (step 102) and identifying, according to the semantic information, a scenario where the robot is currently identified (step 103); finally, determining a current position of the robot according to target sensing data corresponding to the scenario where the robot is located (step 104). In the method, the sensing data used during determining the pose of the robot is not all the sensing data, but is the target sensing data corresponding to the scenario. Therefore, the basis for determining the pose is more targeted, thus further improving the accuracy of the pose.

A developed impact mark analysis apparatus includes: an image acquisition unit configured to obtain at least one first image by photographing impact marks that are developed, and to obtain a second image of impact marks at a crime scene that are developed from evidence at the crime scene; an outliner configured to outline the at least one first image to obtain at least one first outline image, and to outline the second image to obtain a second outline image; a database configured to store the first outline image corresponding to related tool characteristic information; a matching unit configured to search the database for the first outline image determined to be similar to the second outline image and match them with each other; a display unit; and a user input unit.

A computer-implemented method of learning sensory media association includes receiving a first type of nontext input and a second type of nontext input; encoding and decoding the first type of nontext input using a first autoencoder having a first convolutional neural network, and the second type of nontext input using a second autoencoder having a second convolutional neural network; bridging first autoencoder representations and second autoencoder representations by a deep neural network that learns mappings between the first autoencoder representations associated with a first modality and the second autoencoder representations associated with a second modality; and based on the encoding, decoding, and the bridging, generating a first type of nontext output and a second type of nontext output based on the first type of nontext input or the second type of nontext input in either the first modality or the second modality.

The Loupe system defines Loupe Visual Art DNA for art images to be presented to a user so as to maximize and customize the user experience in viewing art images delivered onto digital displays, TVs and other screens facilitating the artwork transition with and without human interaction. The Loupe system recommendations engine utilizes both human and machine curated data to determine factors of art images that will appeal to a user viewing the images. The Loupe system gathers data about visual perception, historical and academic provenance, and emotion or intention represented in an image. The gathered data is analyzed through deep learning and AI algorithms to inform recommendations and select art images to be presented to a user. The user may purchase fine art prints or select originals of the artwork image displayed, if the artist elects to make it available for sale, presented from the Loupe integrated electronic marketplace.

A photographing method and device, a storage medium and an electronic apparatus are disclosed. The method includes the following. A current photographing scene is determined based on a current preview image. Based on the current photographing scene, pre-stored historical adjustment information of a photographing parameter matching the current photographing scene is acquired. Current adjustment information is determined based on the historical adjustment information. Adjustment is performed on the current preview image on the basis of the current adjustment information, and an adjusted current preview image is output.

The present disclosure provides systems and methods that include or otherwise leverage an artificial intelligence system and/or provide user interface mechanisms particularly suited for interacting and/or interfacing with an artificial intelligence system. A computing system can include a camera, a light-emitting device, and an artificial intelligence system that comprises one or more machine-learned models. The computing system can include a processor and one or more non-transitory computer-readable media that stores instructions that, when executed, cause the processor to obtain an image of a scene captured by the camera; generate an attention output that describes at least one region of the scene that includes a subject of a processing operation performed by the artificial intelligence system; and control the light-emitting device to emit light onto or adjacent a region of the scene that includes the subject of the processing operation performed by the artificial intelligence system.

The present disclosure provides a fine-grained image recognition method, an electronic device and a computer readable storage medium. The method comprises the steps of feature extraction, calculation of feature discriminant loss function, calculation of feature diversity loss function and calculation of model optimization loss function. The present disclosure comprehensively considers influences of factors such as a large intra-class difference, a small inter-class difference, and a great influence of background noise of the fine-grained image, and makes constrains such that the feature maps belonging to each class are discriminative and have the features of corresponding class, thus reducing the intra-class difference, decreasing the learning difficulty and learning better discriminative features. The constraints make the feature maps belonging to each class have a diversity, which increases the inter-class difference, achieves a good result, and is easy for practical deployment, thereby obviously improving the effect of multiple fine-grained image classification tasks.

An information processing apparatus includes a processor configured to: obtain plural images each including any of plural objects; and output report information that is information generated based on an analysis result regarding the plural objects in the plural images, and that is information for reporting according to which of two or more objects, among the plural objects, included in an image the image is to be corrected.