Hewlett-Packard Development Company, L.p.

Google Inc.

Hao Tang

Daniel R Tretter

Qian Lin

Ullas Gargi

Vivek Kwatra

Peng Wu

Feng Tang

Mehmet Emre Sargin

Embodiments disclosed herein relate to image prioritization in a collage shape. In one embodiment, image positions are determined within a collage shape based on an importance level map of the collage shape and priorities of the images to be positioned within the collage shape. A collage may be created with the collage images in the determined positions.

Image prioritization in a collage shape

Examples disclosed herein relate to creating an image collage in a semantic theme based shape. For example, a processor may determine a semantic theme associated with an image collection, select a shape associated with the semantic theme, and create a collage of at least a subset of the image collection in the selected shape. The processor may output the created collage.

Semantic theme based shape collage representation for an image collection

A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.

Learning sports highlights using event detection

A method and system characterizes an image of an object. A plurality of interest points are detected within a first image and a local image feature descriptor is built for at least some of the interest points, including mapping information about the interest points according to at least circular distribution information.

Local image feature descriptors according to circular distribution information

A system and a method are disclosed for generating a saliency map of an image. The method includes receiving image data representative of image forming elements of an image and determining saliency values for image forming elements by an iterative method. The iterative method includes computing a norm of the image data, computing values of deviation from the norm of the image data of the image forming elements, identifying the image forming elements corresponding to the image data having magnitudes of deviation that meet a pre-determined condition, assigning saliency values to the identified image forming elements based on the values of deviation, and repeating the computing the norm and deviation, identifying image forming elements and assigning saliency values using the image data of image forming elements that have no assigned saliency value. A saliency map of the image based on the assigned saliency values.

System and method for saliency map generation

A method for auto-cropping includes, creating a saliency map corresponding to a digital image, the saliency map indicating a relevance of pixels within the digital image with saliency values within a number range, a lower end of the number range being less than zero and an upper end of the number range being greater than zero. The method further includes, analyzing the saliency map to find a potential cropping rectangle, the potential cropping rectangle having a maximum sum of saliency values within borders of the rectangle.

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Mountain View, CA, United States of America

Hao Tang