Abstract: Systems and techniques are disclosed for automatically creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame. In an embodiment, this involves determining emotional alignment scores and eye scores for the individual frames of the video clip. The emotional alignment scores for the frames are determined by assessing the faces in each of the frames with respect to an emotional characteristic (e.g., happy, sad, neutral, etc.). The eye scores for the frames are determined based on assessing the states of the eyes (e.g., fully open, partially open, closed, etc.) of the faces in individual frames. Comprehensive scores for the individual frames are determined based on the emotional alignment scores and the eye scores, and the frame having the best comprehensive score is selected as the base frame.

Abstract: Systems and techniques are disclosed for automatically creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame. In an embodiment, this involves determining emotional alignment scores and eye scores for the individual frames of the video clip. The emotional alignment scores for the frames are determined by assessing the faces in each of the frames with respect to an emotional characteristic (e.g., happy, sad, neutral, etc.). The eye scores for the frames are determined based on assessing the states of the eyes (e.g., fully open, partially open, closed, etc.) of the faces in individual frames. Comprehensive scores for the individual frames are determined based on the emotional alignment scores and the eye scores, and the frame having the best comprehensive score is selected as the base frame.

Abstract: Systems and techniques are disclosed for automatically creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame. In an embodiment, this involves determining emotional alignment scores and eye scores for the individual frames of the video clip. The emotional alignment scores for the frames are determined by assessing the faces in each of the frames with respect to an emotional characteristic (e.g., happy, sad, neutral, etc.). The eye scores for the frames are determined based on assessing the states of the eyes (e.g., fully open, partially open, closed, etc.) of the faces in individual frames. Comprehensive scores for the individual frames are determined based on the emotional alignment scores and the eye scores, and the frame having the best comprehensive score is selected as the base frame.

Abstract: Systems and methods disclosed herein provide automatic and intelligent video sorting in the context of creating video compositions. A computing device sorts a media bin of videos in the user's work area based on similarity to the videos included in the video composition being created. When a user selects or includes a particular video on the composition's timeline, the video is compared against the entire video collection to change the display of videos in the media bin. In one example, videos that have similar tags to a selected video are prioritized at the top. Only a subset of frames of each of the videos are used to use to identify video tags. Intelligently selecting tags using a subset of frames from each video rather than using all frames enables more efficient and accurate tagging of videos, which facilitates quicker and more accurate comparison of video similarities.

Abstract: Systems and techniques are disclosed for automatically creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame. In an embodiment, this involves determining emotional alignment scores and eye scores for the individual frames of the video clip. The emotional alignment scores for the frames are determined by assessing the faces in each of the frames with respect to an emotional characteristic (e.g., happy, sad, neutral, etc.). The eye scores for the frames are determined based on assessing the states of the eyes (e.g., fully open, partially open, closed, etc.) of the faces in individual frames. Comprehensive scores for the individual frames are determined based on the emotional alignment scores and the eye scores, and the frame having the best comprehensive score is selected as the base frame.

Abstract: Systems and methods disclosed herein provide automatic and intelligent video sorting in the context of creating video compositions. A computing device sorts a media bin of videos in the user's work area based on similarity to the videos included in the video composition being created. When a user selects or includes a particular video on the composition's timeline, the video is compared against the entire video collection to change the display of videos in the media bin. In one example, videos that have similar tags to a selected video are prioritized at the top. Only a subset of frames of each of the videos are used to use to identify video tags. Intelligently selecting tags using a subset of frames from each video rather than using all frames enables more efficient and accurate tagging of videos, which facilitates quicker and more accurate comparison of video similarities.

Abstract: Various embodiments calculate a score for each frame of a video segment based on various subject-related factors associated with a subject (e.g., face or other object) captured in a frame relative to corresponding factors of the subject in other frames of the video segment. A highest-scoring frame from the video segment can then be extracted based on a comparison of the score of each frame of the video segment with the score of each other frame of the video segment, and the extracted frame can be transcoded as an image for display via a display device. The score calculation, extraction, and transcoding actions are performed automatically and without user intervention, which improves previous approaches that use a primarily manual, tedious, and time consuming approach.

Abstract: Various embodiments calculate a score for each frame of a video segment based on various subject-related factors associated with a subject (e.g., face or other object) captured in a frame relative to corresponding factors of the subject in other frames of the video segment. A highest-scoring frame from the video segment can then be extracted based on a comparison of the score of each frame of the video segment with the score of each other frame of the video segment, and the extracted frame can be transcoded as an image for display via a display device. The score calculation, extraction, and transcoding actions are performed automatically and without user intervention, which improves previous approaches that use a primarily manual, tedious, and time consuming approach.