Systems and methods for recognizing a face are disclosed and includes receiving images of faces; generating feature vectors of the images; generating clusters of feature vectors each with a centroids or a cluster representative; for a query to search for a face, generating corresponding feature vectors for the face and comparing the feature vector with the centroids of all clusters; for clusters above a similarity threshold, comparing cluster members with the corresponding feature vector; and indicating as matching candidates for cluster members with similarity above a threshold.

A graphical user interface (GUI) for forming hierarchically arranged clusters of items and operating thereupon through an electronic device equipped with an input-device and a display-screen is provided. The GUI comprises a first area configured to display a graphical-tree representation having a plurality of hierarchical levels, each of said level corresponds to at least one cluster of content-items formed by execution of a machine-learning classifier over a plurality of input content items. A second area is configured to display a dataset corresponding to the content-items classified within the clusters. A third area is configured to display a plurality of types of content representations with respect to each selected cluster, said representations corresponding to content-items classified within the cluster.

One or more image parameters of an image may be analyzed using a hierarchical set of models. Executing individual models in the set of models may generate outputs from analysis of different image parameters of the image. Inputs of one or more of the models may be conditioned on a set of outputs derived from one or more preceding model in the hierarchy.

A knowledge base (KB) is generated and used to classify images. The knowledge base includes a number subcategories of a specified category. Instead of obtaining images just based on a category name, structured and unstructured data sources are used to identify subcategories of the category. Subcategories that are determined to not be relevant to the category may be removed. The remaining data may be used to generate the KB. After identifying the relevant subcategories, representative images are obtained from one or more image sources based on the subcategories identified by the KB. The obtained images and the KB are then used to train an image classifier, such as a neural network or some other machine learning mechanism. After training, the neural network might, for example, classify an object within an image of a car, as a car, but also as a particular brand and model type.

A method for detecting an object includes inputting information of a moving object included in a plurality of images and generating a regression tree. In response to input of a new image, the system communicates information of a moving object included in the newly inputted image into the regression tree, and determines a size of a person included in the new image based on a resultant value of the regression tree.

A method, computer readable medium, and system are disclosed for generating a Gaussian mixture model hierarchy. The method includes the steps of receiving point cloud data defining a plurality of points; defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map; and adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.

In the multi-target detection and tracking method, lidar (2D laser scanner) scans point cloud data of surroundings and transfers the collected data to the edge server. Then, the edge server uploads the data to the cloud. After obtaining the lidar data, point clouds of footsteps are extracted through dynamic point extraction, point clustering, and random forest model, respectively. Footsteps are matched to form human tracking trajectory by using trajectory matching. After the tracking process, the walking information is published to the users, in a visual form. Meanwhile, the gait parameters are saved into files, including walking speed and step length, when human is detected. Comparing to the visual sensor based human tracking methods, the present invention employs lidar to avoid the interference of ambient light, which leads to easier implementation and larger universality, especially for multi-target scenarios.

An approach is provided for large scale vehicle routing. The approach involves, for example, receiving a plurality of plans, wherein a plan of the plurality of plans assigns a vehicle, a driver of the vehicle, or a combination thereof a set of rides to traverse. The approach also involves clustering the plurality of plans into one or more clusters based on a proximity measure. The proximity measure indicates a proximity of a first plan of the plurality of plans to a second plan of a plurality of plans. The approach further involves, for each cluster of the one or more clusters, separately computing a solution to a multiple vehicle routing problem for the set of rides in said each cluster.

Systems and methods that may be used to determine that input form field data is accurate or not, and associate a level of confidence with that determination. The systems and methods may use a multi part confidence model that uses inter-field correlation to tie the correctness of a particular field to the pattern of values seen in other fields of the document the field data is input from.

According to embodiments illustrated herein there is provided a method for detecting one or more topic transitions in a multimedia content. The method includes identifying, one or more frames from a plurality of frames of the multimedia content based on a comparison between one or more content items in a first frame of the plurality of frames, and the one or more content items in a first set of frames of the plurality of frames. The method further includes determining at least a first score, and a second score for each of the one or more frames. Additionally, the method includes determining a likelihood for each of the one or more frames based at least on the first score, and the second score, wherein the likelihood is indicative of a topic transition among the one or more frames.