This application provides an image recognition method and apparatus, an electronic device, and a computer-readable storage medium, and relates to the field of artificial intelligence technologies. The method includes obtaining feature information corresponding to a target object in an image to be recognized, the feature information comprising blur degree information, local feature information, and global feature information; determining a category of the target object based on the feature information, and determining a confidence level corresponding to the target object; and obtaining target information corresponding to the image to be recognized according to the category of the target object and the confidence level.

A system that recommends cosmetic, dermatological, or fashion items based on photos taken of a person based in part or entirely on their skin color and other defining characteristics like hair color, eye color, and/or face shape. The system includes a device with a camera and a light source capable of producing multiple intensities of light, the device running a program that instructs the user with real time feedback on how to adjust their face, phone positioning or location in order for the application to capture a set of two or more optimal photos of their face. When optimal ambient lighting is found, the program captures multiple photos, varying the light source over the different captures. Calibrated color data is calculated by comparing how the brightness and color of the diffuse reflection on the skin of the user changes compared to the brightness and color of the specular reflection of the light source in the user's eye.

A system that recommends cosmetic, dermatological, or fashion items based on photos taken of a person based in part or entirely on their skin color and other defining characteristics like hair color, eye color, and/or face shape. The system includes a device with a camera and a light source capable of producing multiple intensities of light, the device running a program that instructs the user with real time feedback on how to adjust their face, phone positioning or location in order for the application to capture a set of two or more optimal photos of their face. When optimal ambient lighting is found, the program captures multiple photos, varying the light source over the different captures. Calibrated color data is calculated by comparing how the brightness and color of the diffuse reflection on the skin of the user changes compared to the brightness and color of the specular reflection of the light source in the user's eye.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

A method for automatic extraction of data from a graph, including text area locating and text box classification; locating of coordinate axes, and locating of the positions of hatch marks on the coordinate axes; legend locating and information extraction; extracting corresponding bar or polyline connected components according to legend color, and filtering and classification; determining key points on the X-axis and locating a corresponding X-axis label for each key point; locating key points of the bars and polyline according to the X-axis key points, determining labeled numerical text boxes that correspond to the key points, and identifying the numerical text; calculating a corresponding value for each pixel, and estimating corresponding values of the key points of the bars or polyline; determining a final result according to a difference between the estimated values and the recognized labeled values.

A learning unit derives, from a target image including at least one tubular structure, in a case where an image for learning and ground-truth data of a graph structure included in the image for learning are input to an extraction model which extracts a feature vector of a plurality of nodes constituting a graph structure of the tubular structure, a loss between nodes on the graph structure included in the image for learning on the basis of an error between a feature vector distance between nodes belonging to the same graph structure and a topological distance which is a distance on a route of the graph structure between the nodes, and performs learning of the extraction model on the basis of the loss.

A learning unit derives, from a target image including at least one tubular structure, in a case where an image for learning and ground-truth data of a graph structure included in the image for learning are input to an extraction model which extracts a feature vector of a plurality of nodes constituting a graph structure of the tubular structure, a loss between nodes on the graph structure included in the image for learning on the basis of an error between a feature vector distance between nodes belonging to the same graph structure and a topological distance which is a distance on a route of the graph structure between the nodes, and performs learning of the extraction model on the basis of the loss.

An activity tracking device for tracking and incentivizing personal goals. One embodiment of the activity tracking device is formed as a fitness smart watch with sliders for tracking water and fruit/vegetable intakes, and with the typical smart watch features, including a pedometer, heart-rate monitor, altimeter, stop watch, vibrating notifications, alerts, etc. Another embodiment of the activity tracking device is formed as a band that would have no automated tracking, however users could choose from different bands for tracking their goals for various activities like drinking enough water, fruit and vegetable intake, daily exercises, meditation breaks during the day and more. A preferred embodiment of the activity tracking device is formed as a computer device having computer application thereon that provides a method of tracking and incentivizing personal goals and provides a plurality of electronic touch screen pages that are adapted to allow a user to create goal monitoring electronic pages that allows the user to input data correlating to the progression towards personal goals, and to which graphic images are created to provide the percent of completion of their goals.

A sketch-based image retrieval method, device and system, to improve accuracy of image searching from a scene sketch image. For example, the image retrieval method, device and system can be used to retrieve a target scene image from a collection of stored images in a storage (i.e., an image collection). The image retrieval method includes: segmenting the scene sketch image using an image segmentation module into semantic object-level instances, and fine-grained features are obtained for each object instance, generating an attribute graph which integrates the fine-grained features for each semantic object instance detected from the query scene sketch image, generating a feature graph by using a graph encoder module from the attribute graph, and computing a similarity or distance between the feature graphs of the query scene sketch image and the scene images in the image collection by a graph matching module and the most similar scene images are returned.

A sketch-based image retrieval method, device and system, to improve accuracy of image searching from a scene sketch image. For example, the image retrieval method, device and system can be used to retrieve a target scene image from a collection of stored images in a storage (i.e., an image collection). The image retrieval method includes: segmenting the scene sketch image using an image segmentation module into semantic object-level instances, and fine-grained features are obtained for each object instance, generating an attribute graph which integrates the fine-grained features for each semantic object instance detected from the query scene sketch image, generating a feature graph by using a graph encoder module from the attribute graph, and computing a similarity or distance between the feature graphs of the query scene sketch image and the scene images in the image collection by a graph matching module and the most similar scene images are returned.