Contextual Video Frame Retrieval for Preview Image Selection
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Solution Overview
Problem
Selecting representative images or clips from digital content, such as videos, is a difficult and time-consuming task, requiring extensive review of long content to identify suitable preview images.
Innovation Solution
A system analyzes multimedia content to identify contextually relevant frames using machine learning techniques, including shot detection, key frame extraction, and relevance scoring, utilizing models like PySceneDetect, ResNet-50, and CLIP for visual and textual similarity, to generate high-quality preview images and cover art efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of digital content is performed to identify preview images, then representative frames can be selected, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that uses machine learning models (ResNet-50 for frame embedding, CLIP for text-image similarity) to automatically analyze video content and identify representative frames, thereby eliminating time-consuming manual intervention while maintaining or improving selection quality
Solution Approach 2:
The patent introduces intermediate processing components including shot detection algorithms that segment video into meaningful units, frame embedding models that convert visual data into comparable representations, and relevance scoring mechanisms that bridge video content with search queries, enabling automated intelligent selection without direct human intervention
2Loss of information
If extensive review of long digital content is performed, then contextually relevant frames can be identified, but the complexity and difficulty of the task increase
Solution Approach 1:
The patent segments long video content into smaller shot units using shot detection algorithms, then processes each shot independently to identify key frames. This segmentation approach maintains contextual relevance within each shot while reducing the overall complexity of analyzing entire hours of content at once
Solution Approach 2:
The patent performs preliminary shot detection and key frame extraction before conducting relevance scoring against search queries. This preliminary organization of content into structured segments with representative frames simplifies subsequent analysis and reduces the complexity of the overall task
3Productivity
If automated frame selection is implemented, then time efficiency improves, but the ability to understand contextual relevance without user knowledge is reduced
Solution Approach 1:
The patent substitutes human contextual understanding with automated machine learning models, specifically using CLIP (Contrastive Language-Image Pre-training) that has been pre-trained on vast datasets to understand semantic relationships between images and text, enabling the system to autonomously evaluate contextual relevance without requiring user knowledge of the content
Solution Approach 2:
The patent implements a relevance scoring mechanism that compares video frame embeddings with search query embeddings, providing automated feedback on contextual relevance. This feedback loop enables the system to iteratively refine frame selections based on how well they match the semantic meaning of search queries, maintaining high contextual understanding speed
Data Source
AI summary
Systems, devices, and methods are provided for searchability and discoverability of contextually relevant frames within digital content. Digital content, such as videos, may be segmented to identify a plurality of shots. Discoverability may be performed by identifying key frames of the digital content and using a contrastive language-image pre-training (CLIP) model to determine contextual relevance of a frame or shot to textual information associated with the digital content. Searchability may be performed by receiving search parameters and applying various filters to digital content to identify frames or shots that satisfy a user's search query.