Abstract: In implementations of proficiency based tutorial modification, a computing device implements a tutorial system to receive a user modification of a digital image. A difference between the user modification and an application modification of the digital image is determined. The tutorial system generates a proficiency score for an editing tool based on the difference between the user modification and the application modification, and the proficiency score indicates the user's proficiency in using the editing tool. The tutorial system generates a pre-modified input image for a tutorial depicting a modification applied to an input image to be modified in the tutorial using the editing tool based on the proficiency score for the editing tool being greater than a proficiency threshold.

Abstract: Techniques are disclosed for generating tutorial recommendations to users of image editing applications, based on image content. A methodology implementing the techniques according to an embodiment includes using neural networks configured to determine subject matter of a user provided image and to identify objects in the image. The method also includes selecting one or more proposed tutorials from a database of tutorials. The database is indexed by tutorial subject matter and tutorial object content, and the selection is based on a matching of the determined subject matter to the tutorial subject matter and a matching of the identified objects to the tutorial object content. The method further includes calculating an effectiveness score associated with each of the proposed tutorials, the effectiveness score based on application of the proposed tutorial to the image. The method further includes sorting the proposed tutorials for recommendation to the user based on the effectiveness scores.

Abstract: Methods and systems are provided for generating auto-complete image suggestions. In embodiments described herein, a user image having an edit state is obtained. An edit state can indicate any edits applied by the user to the user image. For the user image, an auto-complete image suggestion is generated. The auto-complete image suggestion includes a representation of the user image with the user-applied edits as well as a set of supplemental edits. Such supplemental edits can be determined from a pre-edited image identified as similar to the user image.

Abstract: Embodiments of the present invention are directed towards providing contextual tags for an image based on a contextual analysis of associated images captured in the same environment as the image. To determine contextual tags, content tags can be determined for images. The determined content tags can be associated with categories based on a contextual classification of the content tags. These associated content tags can then be designated as contextual tags for a respective category. To associate these contextual tags with the images, the images can be iterated through based on how the images relate to the contextual tags. For instance, when an image is associated with a category, the contextual tags classified into that category can be assigned to that image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate dynamic tool-based animated tutorials. In particular, in one or more embodiments, the disclosed systems generate an animated tutorial in response to receiving a request associated with an image editing tool. The disclosed systems then extract steps from existing general tutorials that pertain to the image editing tool to generate tool-specific animated tutorials. In at least one embodiment, the disclosed systems utilize a clustering algorithm in conjunction with image parameters to provide a set of these generated animated tutorials that showcase diverse features and/or attributes of the image editing tool based on measured aesthetic gains resulting from application of the image editing tool within the animated tutorials.

Abstract: Systems and techniques for propagating spot healing edits from a source image to a target image include receiving a source image depicting a face with a healing region and a target image depicting the face. The face is detected in the source image including detecting facial feature points in the source image. The face is identified in the target image including detecting facial feature points in the target image. Facial feature point correspondence is determined between the facial feature points of the source image in the facial feature points of the target image. Region correspondence is determined between regions of the source image and regions of the target image using the facial feature point correspondence. The healing region of the face in the source image is transformed to a corresponding region of the face in the target image using the region correspondence.

Abstract: Certain embodiments involve adaptive search results for multimedia search queries to provide dynamic previews. For instance, a computing system receives a search query that includes a keyword. The computing system identifies, based on the search query, a video file having keyframes with content tags that match the search query. The computing system determines matching scores for respective keyframes of the identified video file. The computing system generates a dynamic preview from at least two keyframes having the highest matching scores.

Abstract: Techniques are disclosed for generating tutorial recommendations to users of image editing applications, based on image content. A methodology implementing the techniques according to an embodiment includes using neural networks configured to determine subject matter of a user provided image and to identify objects in the image. The method also includes selecting one or more proposed tutorials from a database of tutorials. The database is indexed by tutorial subject matter and tutorial object content, and the selection is based on a matching of the determined subject matter to the tutorial subject matter and a matching of the identified objects to the tutorial object content. The method further includes calculating an effectiveness score associated with each of the proposed tutorials, the effectiveness score based on application of the proposed tutorial to the image. The method further includes sorting the proposed tutorials for recommendation to the user based on the effectiveness scores.

Abstract: Embodiments provide systems, methods, and non-transitory computer storage media for providing search result images based on associations of keywords and depth-levels of an image. In embodiments, depth-levels of an image are identified using depth-map information of the image to identify depth-segments of the image. The depth-segments are analyzed to determine keywords associated with each depth-segment based on objects, features, or content in each depth-segment. An image depth-level data structure is generated by matching keywords generated for the entire image with the keywords at each depth-level and assigning the depth-level to the keyword in the image depth-level data structure for the entire image. The image depth-level data structure may be queried for images that contain keywords and depth-level information that match the keywords and depth-level information specified in a search query.

Abstract: Methods and systems are provided for determining intelligent people-groups based on relationships between people. In embodiments, a photo dataset is be processed to represent photos of the photo dataset using vectors. These vectors include the importance of people in the photos. The photos are analyzed to determine similarity between the photos. Similarity is indicative of relationships between the photos of the photo dataset. The similarity is based on the people in the photos. The photos are clustered based on the similarity. In clustering the photos, clustering parameters determined from location information associated with the photos of the photo dataset are used.

Abstract: An image editing program can include a content-aware selection system. The content-aware selection system can enable a user to select an area of an image using a label or a tag that identifies object in the image, rather than having to make a selection area based on coordinates and/or pixel values. The program can receive a digital image and metadata that describes an object in the image. The program can further receive a label, and can determine from the metadata that the label is associated with the object. The program can then select a bounding box for the object, and identify in the bounding box, pixels that represent the object. The program can then output a selection area that surrounds the pixels.

Abstract: Systems and methods disclosed herein for providing people categorization within electronic images. One embodiment involves retrieving an input image from memory. The embodiment further involves determining one or more facial regions in the input image using a facial detection algorithm. The embodiment further involves identifying a number of features for each facial region. The embodiment further involves determining a subject face and a crowd face in the input image based on at least the number of features for the subject face being more than the number of features for the crowd face. The embodiment further involves displaying, on a display device, the subject face identified within the image.

Abstract: In implementations of proficiency based tutorial modification, a computing device implements a tutorial system to receive a user modification of a digital image. A difference between the user modification and an application modification of the digital image is determined. The tutorial system generates a proficiency score for an editing tool based on the difference between the user modification and the application modification, and the proficiency score indicates the user's proficiency in using the editing tool. The tutorial system generates a pre-modified input image for a tutorial depicting a modification applied to an input image to be modified in the tutorial using the editing tool based on the proficiency score for the editing tool being greater than a proficiency threshold.

Abstract: An image editing program can include a content-aware selection system. The content-aware selection system can enable a user to select an area of an image using a label or a tag that identifies object in the image, rather than having to make a selection area based on coordinates and/or pixel values. The program can receive a digital image and metadata that describes an object in the image. The program can further receive a label, and can determine from the metadata that the label is associated with the object. The program can then select a bounding box for the object, and identify in the bounding box, pixels that represent the object. The program can then output a selection area that surrounds the pixels.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing an animated tutorial based on determining modifications made to a digital image. For example, the disclosed systems can determine modifications made to a digital image based on comparing an initial state of a digital image with a modified state of the digital image and/or based on analyzing an action history within a digital image editing application. The disclosed systems can generate an animated tutorial based on the determined modifications and can further generate a deep link that references the animated tutorial. In addition, the disclosed systems can provide the animated tutorial to a social networking system together with the deep link to cause devices to execute a digital image editing application and present the animated tutorial upon selection of the deep link.

Abstract: An image editing program can include a content-aware selection system. The content-aware selection system can enable a user to select an area of an image using a label or a tag that identifies object in the image, rather than having to make a selection area based on coordinates and/or pixel values. The program can receive a digital image and metadata that describes an object in the image. The program can further receive a label, and can determine from the metadata that the label is associated with the object. The program can then select a bounding box for the object, and identify in the bounding box, pixels that represent the object. The program can then output a selection area that surrounds the pixels.

Abstract: Systems and techniques for propagating spot healing edits from a source image to a target image include receiving a source image depicting a face with a healing region and a target image depicting the face. The face is detected in the source image including detecting facial feature points in the source image. The face is identified in the target image including detecting facial feature points in the target image. Facial feature point correspondence is determined between the facial feature points of the source image in the facial feature points of the target image. Region correspondence is determined between regions of the source image and regions of the target image using the facial feature point correspondence. The healing region of the face in the source image is transformed to a corresponding region of the face in the target image using the region correspondence.

Abstract: Systems and methods disclosed herein for providing people categorization within electronic images. One embodiment involves retrieving an input image from memory. The embodiment further involves determining one or more facial regions in the input image using a facial detection algorithm. The embodiment further involves identifying a number of features for each facial region. The embodiment further involves determining a subject face and a crowd face in the input image based on at least the number of features for the subject face being more than the number of features for the crowd face. The embodiment further involves displaying, on a display device, the subject face identified within the image.

Abstract: Embodiments relate to automatic perspective and horizon correction. Generally, a camera captures an image as an image file. Capture-time orientation data from one or more sensors is used to determine the camera's attitude with respect to a defined reference frame. The orientation data and/or attitude can be registered into metadata of the image file and used to generate axis lines representative of the camera's reference frame. A reference line such as a horizon can be automatically identified from detected line segments in the image that align with one of the axis lines within a predetermined angular threshold. The reference line can be used to generate a camera transformation from a starting orientation reflected by the camera attitude to a transformed orientation that aligns the reference line with the reference frame. The transformation can be applied to the image to automatically correct perspective distortion and/or horizon tilt in the image.

Abstract: In implementations of content-adaptive guided tutorial generation, a computing device implements a guided tutorial generation module of an image-editing application that generates a content-adaptive guided tutorial based on input content. The content-adaptive guided tutorial provides instructions on how to interact with image-editing controls of the image-editing application, where the editing controls are selectable to initiate modifications to input parameters of the input content. The guided tutorial generation module receives the input content, and creates copy content that replicates content parameters of the input content as modifiable content parameters.