The present disclosure discloses a photo processing method and an apparatus for grouping photos into photo albums based on facial recognition results. The method includes: performing face detection on multiple photos, to obtain a face image feature set, each face image feature in the face image feature set corresponding to one of the multiple photos; determining a face-level similarity for each pair of face image features in the face image feature set; determining a photo-level similarity between each pair of photos in the multiple photos in accordance with their associated face-level similarities; generating a photo set for each target photo in the multiple photos, wherein any photo-level similarity between the target photo and another photo in the photo set exceeds a predefined photo-level threshold; and generating a label for each photo set using photographing location and photographing time information associated with the photos in the photo set.

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Disclosed herein is a method of determining a user profile based on a set of user-selected images, a method of selecting images from an image database of digital images based on a user profile, a computer system and a computer program product. The method of determining a user profile comprises obtaining a set of reference images, wherein each of the reference images is associated with a category from a plurality of categories; determining a sample feature vector for a sample image and a reference feature vector for each of the reference images, wherein the feature vector of an image is associated to features of the image; determining a similarity metric between the sample image and each of the reference images based on the sample feature vector and the reference feature vectors; selecting nearest reference images for each category, wherein the similarity metric between the sample image and a nearest reference image meets a minimum assignment similarity criterion and a maximum assignment similarity criterion; and determining the user profile by calculating an assignment probability for each category based on the similarity metrics between the sample image and the nearest reference images of the respective category.

According to an example, intent based clustering may include generating a plurality of clusters based on an analysis of categories of features of data used to generate the clusters with respect to an order of each of the features by determining whether a number of samples of the data for a category of the categories meets a specified criterion.

An image recognition device has a database in which pieces of feature data of a plurality of objects are registered while divided into classes for each of the plurality of objects; an identification unit that identifies an unknown object by evaluating which feature data of the class registered in the database is most similar to feature data obtained from an image of the unknown object, and a feature data registration unit that registers feature data in the database. The database is capable of setting a plurality of classes to an identical object. The feature data registration unit, in adding new feature data with respect to a first object already registered in the database, sets a new class other than an existing class with respect to the first object.

Implementations described herein relate to methods, devices, and computer-readable media to generate and provide image-based creations. A computer-implemented method includes obtaining a plurality of episodes, each episode associated with a corresponding time period and including a respective set of images and person identifiers for each image. The method further includes forming a respective cluster for each episode that includes at least two person identifiers. The method further includes determining whether one or more person identifiers are included in less than a threshold number of clusters, and in response, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in. The method further includes merging identical clusters to obtain a plurality of people groups that each include two or more person identifiers and providing a user interface that includes an image-based creation based on a particular people group.

A reception system includes: a visitor recognition unit that recognizes a visitor; a receiving person recognition unit that recognizes a receiving person that corresponds to the visitor; a receiving person contact information storage unit that stores contact information of the receiving person; a notification unit that notifies the receiving person of a visit of the visitor at the contact information of the receiving person stored by the receiving person contact information storage unit; and a receiving person selection unit that selects a substitute receiving person associated with the receiving person in a case where the receiving person is absent when the notification unit notifies the receiving person at the contact information of the receiving person, wherein the notification unit notifies the substitute receiving person selected by the receiving person selection unit when the receiving person is absent.

Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.