Google Inc.

Sally A Goldman

Samy Bengio

Adam L Berenzweig

Jay Michael Ponte

Ron J Weiss

Jason Edward Weston

Ryan Michael Rifkin

Douglas Eck

Thomas Chadwick Walters

Jonathan Albert Wray

Geremy A Heitz, Iii

Clint Chester N Chua

Yoram Singer

Dennis William Strelow

Luca Bertelli

Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.

Generating a playlist

An image cropping technique includes detecting, at a computer system including one or more processors, faces within an image and evaluating, at the computer system, the faces to determine if any of the faces correspond to people within a user's online social network. The technique additionally includes defining, at the computer system, a first crop region including a subset of the faces when at least one face within the subset of the faces corresponds to a first person in the user's online social network and cropping, at the computer system, the image based on the first crop region.

Method and system for automatically cropping images

The present invention relates to methods and systems for automatic image cropping. An automatic image cropping system includes a downsampling system, a saliency analyzer, a spatial scatter, and a face detector configured to detecting one or more faces within the image. The saliency value of each pixel not associated with a detected face within the image is adjusted if one or more faces within the image are detected. A search system defines a saliency region based on a total value of saliency values within the saliency region and on a parameter, where the parameter value is based on a relative importance of a false positive pixel to a false negative pixel. The search system also defines a crop region using an aspect ratio that includes the saliency region and determines a position of the crop region based the saliency values within the crop region by performing a one dimensional search.

A method for determining a salient region of an image is disclosed. For a plurality of different saliency cue functions, a single saliency value is calculated for each pixel in a plurality of adjacent pixels in an image using the saliency cue function, wherein one of the saliency cue functions is based on whether the pixel is in a region of the image whose colors contrast with the region's background and another of the saliency cue functions is based on a foreground and background color models of the image. A classifier is used to calculate a combined single saliency value for each pixel based on the single saliency values for the pixel. The salient region of the pixels is determined with a subwindow search based on the combined single saliency values.

Foreground object detection from multiple images

Self-terminating prediction trees are a generalization of decision trees in which each node is associated with a real-valued prediction. Instead of having a separate pruning phase, a self-terminating tree may be constructed by applying various limits during tree growth that prevent nodes that add little or no additional decision power from being grown within the tree. The prediction tree is learned by performing a penalized empirical risk minimization task, based upon the use of prediction values and functional tree complexity. A separate pruning phase is not required, since the tree self-terminates further growth.

Growth and use of self-terminating prediction trees

The present invention relates to methods and systems for automatic image cropping. An automatic image cropping system includes a downsampling system configured to downsample an image to a threshold number of pixels, a saliency analyzer configured to compute a saliency value for each pixel based on one or more detected color clusters and a spatial scatter of the one or more color clusters, and a face detector configured to detecting one or more faces within the image. However, the saliency value of each pixel not associated with the one or more detected faces within the image is adjusted if one or more faces within the image are detected. Further, a search system is configured to define a saliency region based on a total value of saliency values within the saliency region and on a parameter, where the parameter value is based on a relative importance of a false positive pixel to a false negative pixel. The search system also defines a crop region using an aspect ratio that includes the saliency region and determines a position of the crop region based the saliency values within the crop region by performing a one dimensional search. A cropping system is configured to crop the image based on the one dimensional search. A method for automatic image cropping is also presented.

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Palo Alto, CA, United States of America

Sally A Goldman