Google Inc.

University of California

Taehee Lee

Hartwig Adam

Yuan Hang Li

Stefano Soatto

Jason Chang

Dominick Lim

Kristi Bohl

Vidya Valmikinathan

Jeremy Zhu

Matthew E Kulick

Sylvain Gelly

Brian Potetz

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.

Automatic generation of people groups and image-based creations

One or more devices may receive multiple images; determine one or more classifications for each of the multiple images; generate a confidence score for each classification of the one or more classifications for each of the multiple images. The confidence score may include a value between two numbers and may identify the likelihood that that a particular classification, of the one or more classifications, correctly corresponds to a particular image, of the multiple images. The confidence score may be based on information, associated with a user, and pixel-based classification information. The one or more devices may sort the multiple images by the one or more classifications and by corresponding confidence scores to form sorted multiple images; and provide the sorted multiple images.

Associating classifications with images

A system and computer-implemented method is provided for organizing multiple user submitted results responsive to an image query. A plurality of content submissions may be received from a variety of submitting users, each content submission including an image and an associated label. An image query may provide an image of an object as a request to identify the object. In response to receiving the image query, one or more results of the plurality of content submissions may be identified. A similarity between the labels for each of the one or more results may be determined and used to group the one or more results. Grouped results may be ranked and sorted for accurate and concise presentation to a querying user.

Presenting image search results

We describe an end-to-end visual recognition system, where 'end-to-end' refers to the ability of the system of performing all aspects of the system, from the construction of 'maps' of scenes, or &#x201c;models&#x201d; of objects from training data, to the determination of the class, identity, location and other inferred parameters from test data. Our visual recognition system is capable of operating on a mobile hand-held device, such as a mobile phone, tablet or other portable device equipped with sensing and computing power. Our system employs a video based feature descriptor, and we characterize its invariance and discriminative properties. Feature selection and tracking are performed in real-time, and used to train a template-based classifier during a capture phase prompted by the user. During normal operation, the system scores objects in the field of view based on their ranking.

End-to end visual recognition system and methods

Methods, systems, and apparatus for scoring images related to entities. In one aspect, a method includes identifying images associated with a person, each image being included in one or more resources; obtaining, for each resource that includes one of the images, a quality score that represents a quality of the resource; for each of the images: generating an image resource quality score from the quality scores of the resources that include the image; identifying a set of similar images from the images, each similar image having a measure of similarity to the image that meets a similarity measure threshold; generating an image score based on image resource quality scores of the resources that include the similar images relative to image resource quality scores of the resources that include each of the images; and generating an image authority score based on the image resource quality score and the image score.

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Los Angeles, CA, United States of America

Taehee Lee