Video Preview Frame Selection Using Semantic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Video hosting systems face challenges in accurately representing longer-form videos, as traditional preview methods fail to convey the full content, making it difficult for users to determine if a video contains desired content without watching the entire video.
Innovation Solution
The system uses semantic features to identify and select representative frames from video segments, combining semantic and aesthetic scores to generate a summary that reflects the diversity of content, allowing users to preview the video's key moments based on user interest or search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single frame from the beginning of the video is provided as preview, then the preview generation is simple and fast, but the preview fails to accurately represent the full content of longer-form videos
Solution Approach 1:
The video is divided into multiple segments based on shot boundaries detected through frame difference analysis. Each segment represents a distinct scene or topic within the video. This segmentation allows the system to select representative frames from different parts of the video, thereby accurately representing the full content while maintaining manageable processing complexity.
Solution Approach 2:
The system performs preliminary analysis of the video to detect shot boundaries and identify key segments before preview generation. By pre-processing the video to understand its structure and content distribution, the system can efficiently select representative frames that accurately represent the video content without requiring complex real-time analysis during preview generation.
2Loss of information
If multiple representative frames are selected from different video segments, then the preview accurately represents diverse video content, but the computational complexity and processing time increase
Solution Approach 1:
The video is segmented into meaningful portions based on shot boundaries, allowing the system to focus analysis on transition points rather than processing the entire video uniformly. This reduces the effective processing scope while maintaining comprehensive content representation through strategic frame selection from each segment.
Solution Approach 2:
The system selects a limited number of representative frames from key segments rather than analyzing every frame. By focusing computational resources on critical segments identified through shot boundary detection, the system achieves comprehensive content representation with reduced processing time compared to uniform full-video analysis.
3Ease of operation
If representative frames are selected based on semantic concepts and aesthetic scores, then the preview quality and relevance improve, but the processing complexity and computational resources required increase
Solution Approach 1:
Different selection criteria are applied to different segments based on their characteristics. Semantic concept analysis and aesthetic scoring are used to evaluate frames within each segment, allowing the system to prioritize frames that best represent the local content and quality standards of each particular video portion.
Solution Approach 2:
The system uses multiple parameters (semantic concept relevance, aesthetic quality scores) to evaluate and rank candidate frames. By changing and combining these parameters, the system can objectively identify frames that simultaneously satisfy content relevance and visual quality requirements, improving preview usefulness while maintaining systematic processing.
Data Source
AI summary
A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.