Video Segment Retrieval Using Embeddings for Relevant Frame Search
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Solution Overview
Problem
Existing search algorithms often provide suboptimal relevance in search results, leading to inefficiencies in computational resources and time consumption due to irrelevant results being buried in larger quantities of less relevant data, necessitating additional filtering efforts.
Innovation Solution
A computer-implemented method using machine-learned models to generate embeddings for search queries and video frames, determining relevance scores, and clustering similar frames to provide precise retrieval of relevant frame segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search algorithms are used to retrieve video frames, then the search process is simple, but the relevance of search results becomes suboptimal and time consumption increases
Solution Approach 1:
The system pre-processes video frames by extracting features and generating embeddings before actual search queries are received. Video frames are clustered into segments and annotated with metadata in advance, so when a search query arrives, the system can quickly retrieve pre-processed relevant frames without performing complex analysis during the search operation itself.
Solution Approach 2:
The patent introduces an intermediary representation layer using embeddings and relevance scores as mediators between the search query and video frames. Instead of directly comparing queries to raw video frames, the system uses machine learning models to generate query embeddings and compute relevance scores, which then filter and rank frames efficiently, improving both speed and accuracy.
2Reliability
If traditional search algorithms are used, then the system is simpler, but computational resource use becomes excessive
Solution Approach 1:
The system extracts and pre-computes only the essential features and embeddings of video frames, separating the computationally intensive feature extraction from the search operation. By taking out and storing these pre-processed representations, the system avoids repeatedly performing expensive computations during search, reducing overall computational resource consumption while maintaining high search result quality.
Solution Approach 2:
The patent transforms video frames into a different parameter space using embeddings and relevance scores. This parameter transformation allows the system to work with compressed, more efficient representations rather than raw pixel data during search operations, significantly reducing computational requirements while improving search effectiveness.
3Productivity
If irrelevant results are included in search results, then the retrieval process is faster, but filtering irrelevant results requires additional time and computing resources
Solution Approach 1:
The system uses relevance scores computed from query embeddings and video frame embeddings as feedback to guide the retrieval process. These relevance scores automatically rank and filter frames, allowing the system to quickly identify and return only the most relevant results without requiring additional manual or computational filtering steps, thus improving both speed and efficiency.
Data Source
AI summary
Methods, systems, devices, and non-transitory computer readable media for video frame search and retrieval are provided. The disclosed technology can include receiving a search query. Based on inputting the search query into machine-learned models, a search query embedding can be generated. Based on comparing the search query embedding to video embeddings, video relevance scores can be determined. Relevant video embeddings comprising the video embeddings that are associated with the video relevance scores that satisfy relevance criteria can be determined. Based on comparing the search query embedding to the frame segment embeddings associated with the relevant video embeddings, frame segment relevance scores associated with the frame segment embeddings can be determined. Relevant frame segment embeddings comprising the frame segment embeddings associated with the frame segment relevance scores that satisfy the relevance criteria can be determined. Furthermore, search results associated with frames corresponding to the relevant frame segment embeddings can be generated.