A device for categorising regions in images is disclosed. The device comprising: an input for receiving a first set of images, and defining one or more regions of for each image of the first set of images and a categorisation for the one or more regions, and a second set of images, and a categorisation for each image of the second set; and a processor configured to train a first machine learning algorithm to categorise features in images by: processing the images of the first and second set using the first algorithm to estimate feature regions in the images and a categorisation for each of the feature regions, and training the first algorithm in dependence on the categorisations received for the images of the first and second sets.

A device for categorising regions in images is disclosed. The device comprising: an input for receiving a first set of images, and defining one or more regions of for each image of the first set of images and a categorisation for the one or more regions, and a second set of images, and a categorisation for each image of the second set; and a processor configured to train a first machine learning algorithm to categorise features in images by: processing the images of the first and second set using the first algorithm to estimate feature regions in the images and a categorisation for each of the feature regions, and training the first algorithm in dependence on the categorisations received for the images of the first and second sets.

The present disclosure is directed to systems and methods for performing automated labeling of images. Labeled images can be used to train machine-learned models to infer image attributes such as quality for suggesting user actions.

The present disclosure is directed to systems and methods for performing automated labeling of images. Labeled images can be used to train machine-learned models to infer image attributes such as quality for suggesting user actions.

A method for determining a class to which an input image belongs in an inference process, includes: storing a frequent feature for each class in a frequent feature database; inputting an image as the input image; extracting which class the input image belongs to; extracting a plurality of features that appear in the inference process; extracting one of the features that satisfies a predetermined condition as a representative feature; reading out the frequent feature corresponding to an extracted class from the frequent feature database; extracting one or a plurality of ground features based on the frequent feature and the representative feature; storing a concept data representing each feature in an annotation database; reading out the concept data corresponding to the one or the plurality of ground features from the annotation database; generating explanation information; and outputting the ground feature and the explanatory information together with the extracted class.

Disclosed are a method and a system for determining a response to a digital task in a computer-implemented crowd-sourced environment. The method comprises determining if a number of the plurality of responses to the digital task received meets a pre-determined minimum answer threshold; in response to the number of the plurality of responses to the digital task meeting the pre-determined minimum answer threshold, executing: for each of the plurality of responses generating, by the server, a confidence parameter representing a probability of an associated one of the plurality of responses being correct; ranking the plurality of responses based on the confidence parameter to determine a top response being associated with a highest confidence parameter; and in response to the highest confidence parameter being above a pre-determined minimum confidence threshold, assigning a value of the top response as a label for the digital task and terminating the digital task execution.

A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

In some aspects, a method for object re-identification may include obtaining a first set of images from a first camera, and a second set of images from at least one second camera; determining a first set of features based on the first set of images, the first set of features lying in a first feature space; and determining a second set of features based on the second set of images, the second set of features lying in a second feature space. The method may additionally include determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and determining a common dictionary based on the shared feature space.

In some aspects, a method for object re-identification may include obtaining a first set of images from a first camera, and a second set of images from at least one second camera; determining a first set of features based on the first set of images, the first set of features lying in a first feature space; and determining a second set of features based on the second set of images, the second set of features lying in a second feature space. The method may additionally include determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and determining a common dictionary based on the shared feature space.