Google Inc.

Ameesh Makadia

Sanjiv Kumar

Gideon S Mann

Corinna Cortes

Hartwig Adam

Michael Hemmer

Shanmugavelayutham Muthukrishnan

Teresa Ko

Troy Chinen

Nemanja Petrovic

Sebastian Pueblas

Henry Allan Rowley

Jay Yagnik

Jason Edward Weston

Brian Edmond Brewington

David Kogan

Ming Zhao

Arthur Edmond Blume

Mark A Limber

Evangelos Kokkevis

Andrew Logan Harp

Ehud Rivlin

Won S Chun

Diego Ariel Gertzenstein

Rachel L Petterson

Igor Vytyaz

Anthony Gerald Francis

Corey Michael Joshua Goldfeder

Walter Thatcher Ulrich

Leopoldo Taravilse Diez

Compressing a frame of video includes receiving a frame of a video, identifying a three dimensional (3D) object in the frame, matching the 3D object to a stored 3D object, compressing the frame of the video using a color prediction scheme based on the 3D object and the stored 3D object, and storing the compressed frame with metadata, the metadata identifying the 3D object, indicating a position of the 3D object in the frame of the video and indicating an orientation of the 3D object in the frame of the video.

Video encoding by providing geometric proxies

A computer-implemented method for presenting content based on information detected in real time includes: receiving first information corresponding to detection of at least attendance at a physical location; selecting, in real time with the detection, at least one winning bid based on the first information, the at least one winning bid conditioned on the first information meeting an attendance criterion; and providing at least one winning content item associated with the selected winning bid to a content presentation device at the physical location.

Content presentation based on information detected in real time

A method includes receiving geometric data to be encoded, generating a signature for the geometric data based on the at least one property associated with the geometric data, enumerating a first set of options, enumerating a second set of options, encoding the geometric data using the first option and the second option, decoding the encoded geometric data, determining a performance associated with encoding the geometric data, determining a performance associated with decoding the encoded geometric data, and training a regressor based on the signature, the enumerated first option, the enumerated second option, the performance associated with encoding the geometric data and the performance associated with decoding the encoded geometric data.

Geometry encoder

Methods and systems for interacting with multiple three-dimensional (3D) object data models are provided. An example method may involve receiving a search query comprising a descriptor for an object that is associated with a given category of objects. The method may involve comparing the descriptor to contents of a three-dimensional (3D) object-data-model database that includes contents associated with the given category of objects. Based on the comparison, the method may involve generating a search query result that comprises 3D object data models that describe the object and associated images that describe the object. The method may also involve arranging the 3D object data models and associated images in the search query result in a manner such that the 3D object data models are moveable inline in the search query result, and providing the search query result.

Searching three-dimensional (3D) object data models

Techniques for providing image search templates are provided. An image search template may be associated with an image search query to aid the user in capturing an image that will be appropriate for processing the search query. The template may be displayed as an overlay during an image capturing process to indicate an appropriate image capturing pose, range, angle, or other view characteristics that may provide more accurate search results. The template may also be used in the image search query to segment the image and identify features relevant to the search query. Images in an image database may be clustered using characteristics of the images or metadata associated with the images in order to establish groups of images from which templates may be derived. The generated templates may be provided to users to assist in capturing images to be used as search engine queries.

Providing image search templates

Methods, systems, and apparatus, including computer program products, for ranking search results for queries. The method includes calculating a visual similarity score for one or more pairs of images in a plurality of images based on visual features of images in each of the one or more pairs; building a graph of images by linking each of one or more images in the plurality of images to one or more nearest neighbor images based on the visual similarity scores; associating a respective score with each of one or more images in the graph based on data indicative of user behavior relative to the image as a search result for a query; and determining a new score for each of one or more images in the graph based on the respective score of the image, and the respective scores of one or more nearest neighbors to the image.

Content-based image ranking

Methods, systems, and apparatus, including computer program products, for generating data for annotating images automatically. In one aspect, a method includes receiving an input image, identifying one or more nearest neighbor images of the input image from among a collection of images, in which each of the one or more nearest neighbor images is associated with a respective one or more image labels, assigning a plurality of image labels to the input image, in which the plurality of image labels are selected from the image labels associated with the one or more nearest neighbor images, and storing in a data repository the input image having the assigned plurality of image labels. In another aspect, a method includes assigning a single image label to the input image, in which the single image label is selected from labels associated with multiple ranked nearest neighbor images.

Annotating images

Content presentation based on winning bid and attendance detected at a physical location information in real time

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing joint image-audio queries. In one aspect, a method includes receiving, from a client device, a joint image-audio query including query image data and query audio data. Query image feature data is determined from the query image data. Query audio feature data is determined from the audio data. The query image feature data and the query audio feature data are provided to a joint image-audio relevance model trained to generate relevance scores for a plurality of resources, each resource including resource image data defining a resource image for the resource and text data defining resource text for the resource. Each relevance score is a measure of the relevance of corresponding resource to the joint image-audio query. Data defining search results indicating the order of the resources is provided to the client device.

Search with joint image-audio queries

Methods and systems for 3D shape retrieval are described herein. An embodiment includes receiving a query 3D model, extracting a plurality of features of the query 3D model, comparing the features of the 3D query model against a plurality of features of one or more 3D models in a repository, calculating a similarity score based on the comparing step, ranking the 3D models based on the similarity score and returning one or more 3D models based on the ranking step. The embodiment further includes generating a representation of the query 3D model, calculating a correlation matching function between the representation of the query 3D model and representations of 3D models in the repository, determining a plurality of matching scores between the query 3D model and the 3D models in the repository for each rotational alignment of the 3D models and selecting the highest score from the plurality of matching scores based on the determining step.

Methods and systems for 3D shape matching and retrieval

Growing community of inventors

New York, NY, United States of America

Ameesh Makadia