Thumbnail Video Segmentation Using Feature Boundary Graphs
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Solution Overview
Problem
Conventional video editing tools are tedious and challenging for users due to their reliance on time-based interaction modalities, which require selecting specific video frames or time ranges, leading to inflexible and inefficient interfaces.
Innovation Solution
The implementation of video segmentation techniques using machine learning models to detect features in videos, generating semantically meaningful segments, and providing interactive interfaces that allow users to browse, select, and edit these segments with improved granularity, including default, search, snap point, and thumbnail segmentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional time-based interaction modalities are used for video editing, then users can select specific video frames or time ranges, but the interface becomes inflexible and tedious
Solution Approach 1:
The video is segmented into semantically meaningful segments based on detected features (faces, scenes, audio events, etc.). Users can select and edit these pre-segmented portions directly, eliminating the need to manually navigate through individual frames or time ranges. This segmentation approach simplifies the editing process while maintaining precision.
Solution Approach 2:
Machine learning models act as intermediaries that automatically analyze video content and generate semantic segments. These models process the raw video data and present users with meaningful segments, serving as a mediator between the complex video data and the user's editing needs.
2Productivity
If users manually select video frames for editing, then precise control is achieved, but the process becomes tedious and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically detecting features and generating semantic segments before the user begins editing. This pre-processing creates ready-to-edit segments, so users don't need to spend time manually selecting frames or defining time ranges, thus significantly reducing editing time while maintaining precision.
3Adaptability or versatility
If conventional video editing tools are used, then basic editing operations are available, but the tools are beyond the skill level of many users
Solution Approach 1:
The system provides self-service by automatically performing complex analysis and segmentation tasks that would otherwise require skilled operators. The machine learning models autonomously identify semantic segments based on multiple features, making advanced video editing capabilities accessible to users regardless of their technical expertise.
4Measurement precision
If multiple feature detection models are used to generate semantic segments, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
Multiple feature detection models (face detection, scene detection, audio event detection, etc.) are merged into a unified segmentation system. These models work together to generate comprehensive semantic segments, combining their individual strengths to achieve high segmentation accuracy while managing system complexity through integrated architecture.
Data Source
AI summary
Embodiments are directed to a thumbnail segmentation that defines the locations on a video timeline where thumbnails are displayed. Candidate thumbnail locations are determined from boundaries of feature ranges of the video indicating when instances of detected features are present in the video. In some embodiments, candidate thumbnail separations are penalized for being separated by less than a minimum duration corresponding to a minimum pixel separation (e.g., the width of a thumbnail) between consecutive thumbnail locations on a video timeline. The thumbnail segmentation is computed by solving a shortest path problem through a graph that models different thumbnail locations and separations. As such, a video timeline is displayed with thumbnails at locations on the timeline defined by the thumbnail segmentation, with each thumbnail depicting a portion of the video associated with the thumbnail location.