Video Segmentation Graph for Semantic Editing Boundaries
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Solution Overview
Problem
Conventional video editing is tedious and challenging for many users due to its reliance on time-based interaction modalities that require selecting specific video frames or time ranges, leading to inflexible and inefficient interfaces.
Innovation Solution
Video segmentation is performed using machine learning models to detect features such as transcripts, faces, and scenes, generating segmentations like default, search, snap point, and thumbnail segments, with interactive interfaces for browsing, selecting, and editing semantically meaningful video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional time-based video editing interfaces are used, then users can select and edit video frames, but the editing process becomes tedious and challenging due to inflexible interaction modalities
Solution Approach 1:
The video is automatically segmented into semantically meaningful segments based on detected features such as scenes, objects, actions, and audio events. This segmentation divides the continuous video stream into discrete, meaningful units that users can easily select and edit as complete semantic entities rather than individual frames, thereby simplifying the editing process and reducing time loss.
Solution Approach 2:
The patent introduces an intermediary layer between the raw video data and the user interface in the form of automatically generated segmentations. This intermediary processing layer translates complex video content into structured segments with metadata, which then serve as the basis for simplified user interaction. Users interact with these pre-processed segments rather than raw video frames, improving ease of operation.
2Loss of information
If multiple video segmentations are generated based on different features, then semantic meaning is improved, but the system complexity increases
Solution Approach 1:
The patent merges multiple feature detection results (visual scenes, detected objects, actions, audio events) into a unified segmentation structure. By combining these different feature types and their corresponding segments, the system preserves comprehensive semantic information while presenting a consolidated view to users, reducing the perceived complexity despite processing multiple feature streams.
Solution Approach 2:
The segmentation system is designed to handle multiple types of features and generate multiple segmentation views (e.g., scene-based segmentation, object-based segmentation, action-based segmentation) using a unified framework. This multi-functional approach allows the same system architecture to process different feature types, reducing overall system complexity through reuse of common processing components.
3Productivity
If automatic feature detection is used to generate segmentations, then editing efficiency is improved, but the requirement for machine learning models increases system complexity
Solution Approach 1:
The system performs preliminary automatic feature detection and segmentation generation before the actual editing process. By pre-processing the video content to identify and segment meaningful units based on detected features, the system prepares the data in advance, enabling users to perform editing operations more efficiently without needing to manually analyze video content, thus improving productivity.
Data Source
AI summary
Embodiments are directed to video segmentation based on detected video features. More specifically, a segmentation of a video is computed by determining candidate boundaries from detected feature boundaries from one or more feature tracks; modeling different segmentation options by constructing a graph with nodes that represent candidate boundaries, edges that represent candidate segments, and edge weights that represent cut costs; and computing the video segmentation by solving a shortest path problem to find the path through the edges (segmentation) that minimizes the sum of edge weights along the path (cut costs). A representation of the video segmentation is presented, for example, using interactive tiles or a video timeline that represent(s) the video segments in the segmentation.