Abstract: Implementations relate to automatic sharing of images with designated users over a communication network. In some implementations, a method includes causing display of images in a user interface on a first device, each of the images depicting a different person. The images are obtained from a library of images associated with a first user. A selection of a particular image of the images is received based on user input, and a person identifier is determined indicating a particular person depicted in the selected image. The person identifier is designated as a person sharing criterion. A first image not included in the images is obtained and programmatically analyzed to determine that the first image depicts a person that matches the person sharing criterion, and an access permission is updated to grant access to the first image to a second user of a second device over a communication network.

Abstract: Implementations are related to providing image selection suggestions. In some implementations, a method includes receiving first user input indicative of selection of one or more first images in an image library and determining one or more first image characteristics of the one or more first images. The method further includes identifying one or more second images in the image library. Each image of the one or more second images is associated with at least one second image characteristic that matches at least one of the one or more first image characteristics. The method further includes causing a user interface to be displayed. The user interface includes the one or more second images and enables selection of the one or more second images by a user.

Abstract: Implementations relate to automatic sharing of images with designated users over a communication network. In some implementations, a method includes causing display of images in a user interface on a first device, each of the images depicting a different person. The images are obtained from a library of images associated with a first user. A selection of a particular image of the images is received based on user input, and a person identifier is determined indicating a particular person depicted in the selected image. The person identifier is designated as a person sharing criterion. A first image not included in the images is obtained and programmatically analyzed to determine that the first image depicts a person that matches the person sharing criterion, and an access permission is updated to grant access to the first image to a second user of a second device over a communication network.

Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.

Abstract: Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.

Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.

Abstract: Implementations relate to generating theme-based videos. In some implementations, a computer-implemented method to automatically generate a theme-based video includes obtaining image criteria for a theme from a theme definition, querying an image database to obtain a plurality of images that meet the image criteria, determining that a count of the plurality of images satisfies a threshold based on the theme definition, and generating the theme-based video that includes one or more of the plurality of images.

Abstract: Implementations are related to providing image selection suggestions. In some implementations, a method includes receiving first user input indicative of selection of one or more first images in an image library and determining one or more first image characteristics of the one or more first images. The method further includes identifying one or more second images in the image library. Each image of the one or more second images is associated with at least one second image characteristic that matches at least one of the one or more first image characteristics. The method further includes causing a user interface to be displayed. The user interface includes the one or more second images and enables selection of the one or more second images by a user.

Abstract: Implementations relate to automatic sharing of images with designated users over a communication network. In some implementations, a method includes causing display of images in a user interface on a first device, each of the images depicting a different person. The images are obtained from a library of images associated with a first user. A selection of a particular image of the images is received based on user input, and a person identifier is determined indicating a particular person depicted in the selected image. The person identifier is designated as a person sharing criterion. A first image not included in the images is obtained and programmatically analyzed to determine that the first image depicts a person that matches the person sharing criterion, and an access permission is updated to grant access to the first image to a second user of a second device over a communication network.

Abstract: Implementations are related to providing image selection suggestions. In some implementations, a method includes receiving first user input indicative of selection of one or more first images in an image library and determining one or more first image characteristics of the one or more first images. The method further includes identifying one or more second images in the image library. Each image of the one or more second images is associated with at least one second image characteristic that matches at least one of the one or more first image characteristics. The method further includes causing a user interface to be displayed. The user interface includes the one or more second images and enables selection of the one or more second images by a user.

Abstract: Implementations relate to automatic sharing of images with designated users over a communication network. In some implementations, a method includes causing display of images in a user interface on a first device, each of the images depicting a different person. The images are obtained from a library of images associated with a first user. A selection of a particular image of the images is received based on user input, and a person identifier is determined indicating a particular person depicted in the selected image. The person identifier is designated as a person sharing criterion. A first image not included in the images is obtained and programmatically analyzed to determine that the first image depicts a person that matches the person sharing criterion, and an access permission is updated to grant access to the first image to a second user of a second device over a communication network.

Abstract: Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

Abstract: Implementations relate to removal of one or more images from a view of a plurality of images. In some implementations, a method includes obtaining a plurality of images, programmatically analyzing the plurality of images to determine a plurality of image features, and determining one or more image categories for the plurality of images based on the image features. The method further includes identifying a subset of the plurality of images based on the image categories, wherein each image of the subset is associated with an image category for archival. The method further includes causing a user interface to be displayed that includes one or more images of the subset, receiving user input to archive at least one of the one or more images, and in response to the user input, removing the at least one of the images from a view of the plurality of images.

Abstract: Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.

Abstract: A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.

Abstract: Implementations relate to generating theme-based videos. In some implementations, a computer-implemented method to automatically generate a theme-based video includes obtaining image criteria for a theme from a theme definition, querying an image database to obtain a plurality of images that meet the image criteria, determining that a count of the plurality of images satisfies a threshold based on the theme definition, and generating the theme-based video that includes one or more of the plurality of images.