Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: A method for organizing images from multiple image capture devices includes automatically determining a coarse offset between image capture times recorded in a first image capture device and image capture times recorded in a second image capture device. The coarse offset is determined by a computing a correlation between image counts of images captured by the first image capture device and images captured by the first image capture device. The method also includes adjusting the image capture times of images recorded in the second image capture device by the coarse offset to produce adjusted image capture times for images captured by the second image capture device.

Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: System and method for creating a collection of images are described, the method comprising: receiving images from at least one source of images; processing the images to produce an output collection of images, the processing comprising grouping the images to clusters of related images and selecting the preferred images in the clusters; and outputting the output collection of images, the output collection of images comprising the clusters of related images and indication of the preferred images in the clusters. The system for creating a collection of images comprising: a storage medium to receive images from at least one source of images; a processor to produce an output collection of images by grouping the images to clusters of related images and selecting the preferred images in the clusters; and a collection output medium for outputting the output collection of images.

Abstract: A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Abstract: A method for organizing images from multiple image capture devices includes automatically determining a coarse offset between image capture times recorded in a first image capture device and image capture times recorded in a second image capture device. The coarse offset is determined by a computing a correlation between image counts of images captured by the first image capture device and images captured by the first image capture device. The method also includes adjusting the image capture times of images recorded in the second image capture device by the coarse offset to produce adjusted image capture times for images captured by the second image capture device.

Abstract: A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Abstract: A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Abstract: A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.

Abstract: A computer-implemented method for creating an image product by accurately grouping faces includes receiving an initial set of face groups for a plurality of face images, training classifiers between pairs of face groups in the initial set of face groups using image-product statistics by a computer processor, classifying the plurality of face images by classifiers to output binary vectors for the plurality of face images by the computer processor, calculating a value for an improved similarity function using the binary vectors for each pair of the plurality of face images, grouping the plurality of face images into modified face groups based on values of the binary similarity functions by the computer processor, and creating an image product based at least in part on the modified face groups.

Abstract: A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Abstract: A computer-implemented method for creating an image product by accurately grouping faces includes receiving an initial set of face groups for a plurality of face images, training classifiers between pairs of face groups in the initial set of face groups using image-product statistics by a computer processor, classifying the plurality of face images by classifiers to output binary vectors for the plurality of face images by the computer processor, calculating a value for an improved similarity function using the binary vectors for each pair of the plurality of face images, grouping the plurality of face images into modified face groups based on values of the binary similarity functions by the computer processor, and creating an image product based at least in part on the modified face groups

Abstract: A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Abstract: A computer-implemented method for creating an image product by accurately grouping faces includes receiving an initial set of face groups for a plurality of face images, training classifiers between pairs of face groups in the initial set of face groups using image-product statistics by a computer processor, classifying the plurality of face images by classifiers to output binary vectors for the plurality of face images by the computer processor, calculating a value for an improved similarity function using the binary vectors for each pair of the plurality of face images, grouping the plurality of face images into modified face groups based on values of the binary similarity functions by the computer processor, and creating an image product based at least in part on the modified face groups.

Abstract: System and method are disclosed for creating a photo calendar. A computer storage medium stores images taken in a time period spanning a plurality of capture months. A computer processor automatically divides the images into groups based the capture months, distributes the images in one of the capture month to one or more calendar months according to an adjacency distribution function, and creates a design of a photo calendar comprising a plurality of calendar months and images distributed in the calendar months.

Abstract: A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.

Abstract: A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.