Key Frame Detection for Multi-Modal Video Retrieval Accuracy
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Solution Overview
Problem
Conventional video retrieval methods rely on unreliable human-added metadata, leading to inefficiencies and high costs in processing vast amounts of video data, as they fail to comprehensively recognize and process various information such as video and temporal context.
Innovation Solution
A method and apparatus utilizing a machine learning-enabled key frame detection module to identify key frames based on encoded vectors, segment video data, and generate key frame detection vectors across multiple data domains, including visual, voice-based, and text-based data, to enhance video retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional retrieval methods using hashtags or keywords are used, then the system is simple to operate, but the retrieval quality deteriorates due to reliance on unreliable metadata
Solution Approach 1:
The patent replaces manual metadata tagging (mechanical/human process) with automated machine learning models that analyze video content directly. The encoder-based system automatically extracts features from video frames, audio, and text to generate embeddings, eliminating reliance on unreliable human-added hashtags and keywords while improving retrieval quality through content-based analysis.
Solution Approach 2:
The system enables video data to self-describe through automated feature extraction and embedding generation. Instead of requiring external human annotation, the video content itself generates meaningful representations through the encoder models, allowing the system to serve its own indexing and retrieval needs without external metadata dependency.
2Measurement precision
If comprehensive video data processing is implemented, then retrieval accuracy improves, but processing time increases
Solution Approach 1:
The patent segments video data into discrete frames and processes them individually through encoder models. By dividing the continuous video stream into frame-level units, the system can efficiently process comprehensive visual information without being overwhelmed by the sheer volume of continuous data, enabling accurate retrieval while managing processing time through parallel frame encoding.
Solution Approach 2:
The system performs preliminary encoding of video frames, audio segments, and text into embedding vectors in advance. These pre-computed embeddings are stored and can be quickly retrieved and compared during query processing, eliminating the need for real-time comprehensive analysis during actual retrieval operations, thus improving speed while maintaining accuracy.
3Loss of information
If multiple data domains are processed, then information completeness improves, but computational complexity increases
Solution Approach 1:
The patent employs a unified encoder architecture that handles multiple data domains (visual frames, audio signals, text data) through the same computational framework. The encoder models are designed to process diverse input types and generate consistent embedding representations, enabling comprehensive multi-domain information processing without requiring separate complex systems for each data type, thus improving information completeness while managing computational complexity.
Data Source
AI summary
In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: generating one or more sub video data based on one or more video data; identifying, by a machine learning enabled key frame detecting module having one or more encoders, key frame information based on one or more encoded vectors generated from the one or more sub video data; and segmenting the one or more video data into one or more retrieval video data based on the identified key frame information.