Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that resize a graphical user interface design layout using automatically-generated semantic-aware resize constraints. In particular, systems described herein can analyze the semantics of graphical user interface design elements (e.g., including size, position, type, etc.) and relative positions of the design elements (e.g., hierarchy, offsets, nesting, etc.). The disclosed systems utilizes a heuristics system to the analyzed semantics to generate a set of automatically-defined semantic-aware resize constraints for each graphical user interface design interface. The disclosed systems additionally present a design resize interface that enables designer to manually select constraints to be applied to the design elements. The disclosed systems can toggle between applying manually-selected and automatic semantic-aware resize constraints for the design elements.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that resize a graphical user interface design layout using automatically-generated semantic-aware resize constraints. In particular, systems described herein can analyze the semantics of graphical user interface design elements (e.g., including size, position, type, etc.) and relative positions of the design elements (e.g., hierarchy, offsets, nesting, etc.). The disclosed systems utilizes a heuristics system to the analyzed semantics to generate a set of automatically-defined semantic-aware resize constraints for each graphical user interface design interface. The disclosed systems additionally present a design resize interface that enables designer to manually select constraints to be applied to the design elements. The disclosed systems can toggle between applying manually-selected and automatic semantic-aware resize constraints for the design elements.

Abstract: A computer implemented method and apparatus for automatically identifying a representative image for an image group. The method comprises dividing an image group into one or more clusters based on an average time gap of the image group, wherein the images in the image group are in sequential timestamp order wherein the average time gap is calculated using a time span calculated from the timestamp of a first image in the image group to the timestamp of a last image in the image group; recursively dividing a largest cluster in the one or more clusters to determine a resultant cluster, wherein the resultant cluster comprises no time gaps larger than the average time gap of the resultant cluster; and identifying a representative image from the resultant cluster as an image representative for the image group.

Abstract: A computer implemented method and apparatus for automatically identifying a representative image for an image group. The method comprises dividing an image group into one or more clusters based on an average time gap of the image group, wherein the images in the image group are in sequential timestamp order wherein the average time gap is calculated using a time span calculated from the timestamp of a first image in the image group to the timestamp of a last image in the image group; recursively dividing a largest cluster in the one or more clusters to determine a resultant cluster, wherein the resultant cluster comprises no time gaps larger than the average time gap of the resultant cluster; and identifying a representative image from the resultant cluster as an image representative for the image group.

Abstract: A method includes receiving characteristic information pertaining to respective portions of content from each of a plurality of users. Aggregate characteristic information is updated based on the received characteristic information. The aggregate characteristic information is communicated to a user to assist the user in navigation of the content.

Abstract: A technique that may be used to configure a computer system to automatically search a video and select a frame within the video clip that best represents the content of that video. A frame contained in a video is identified. Luminance values for each pixel in a plurality of pixels contained in the frame are then generated. The frame is then selected as a poster frame for the video if a luminance value for at least one pixel in the plurality of pixels contained in the identified frame is outside a range of luminance values. The range of luminance values may be determined from a luminance value associated with a reference pixel contained in the frame.