Abstract: Techniques for selecting a digital image are disclosed. The techniques may include receiving a first set of digital images, analyzing the first set of digital images to extract first image features from each of the first set of digital images, accessing a user profile, comparing the extracted first image features to a preset list of image features, ranking each digital image of the first set, selecting each digital image having a ranking that exceeds a threshold, assigning a category to each selected digital image based on a comparison of each selected digital image to a category database of digital image categories, displaying each selected digital image with the assigned category, receiving an input from the user in response to the displaying, updating the user profile and the category database based on the input, and selecting at least one subsequent digital image based on the updated user profile and category database.

Abstract: Methods, systems, and techniques for annotating large amounts of data are provided. Example embodiments provide a Scalable Annotation Architecture (a “SAS”), which builds predictive models for an annotation from the ground up, without knowledge of the data. The SAS operates by performing in an iterative fashion a process that seeds training data and hypothesizes a predictive model based upon that data, then sends samples of the data to a crowdsourcing environment to provide selective verification. This process is repeated iteratively until a desired precision is reached and then the model is employed independently in a production system. In one embodiment, the SAS is used to annotate data provided by an open data platform.

Abstract: Techniques for selecting a digital image are disclosed. The techniques may include receiving a first set of digital images, analyzing the first set of digital images to extract first image features from each of the first set of digital images, accessing a user profile, comparing the extracted first image features to a preset list of image features, ranking each digital image of the first set, selecting each digital image having a ranking that exceeds a threshold, assigning a category to each selected digital image based on a comparison of each selected digital image to a category database of digital image categories, displaying each selected digital image with the assigned category, receiving an input from the user in response to the displaying, updating the user profile and the category database based on the input, and selecting at least one subsequent digital image based on the updated user profile and category database.