Video Scene Vector Encoding for Semantic Search
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Solution Overview
Problem
Users face difficulties in finding specific segments of videos that answer their queries due to the abundance of video content online, often requiring them to watch multiple videos before finding the relevant information.
Innovation Solution
An apparatus and method for video searching that splits videos into scenes, encodes textual descriptors using deep neural networks and a Universal Sentence Encoder, and matches user queries with scene vectors to provide relevant segments based on semantic similarity, allowing for efficient retrieval of video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search through multiple videos to find relevant segments, then they can find the information they need, but it consumes excessive time and effort
Solution Approach 1:
The system performs preliminary actions by automatically generating textual descriptors for video scenes and encoding them into vectors before user search. This pre-processing enables instant semantic matching when users submit queries, eliminating the need for manual video browsing and significantly reducing search time while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated semantic search system. Instead of users manually watching and analyzing videos, the system uses vector encoding and similarity comparison algorithms to automatically match user queries with relevant video segments, substituting human cognitive effort with computational processes.
2Measurement precision
If the system encodes all textual descriptors into video scene vectors for comprehensive search, then search accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments videos into discrete scenes and generates separate textual descriptors for each scene. This segmentation allows the system to process and encode manageable units independently, reducing overall computational complexity while maintaining comprehensive search capability across the entire video content.
Solution Approach 2:
The patent transforms textual descriptors into vector representations, changing the parameter space from text to numerical vectors. This transformation enables efficient similarity computation using mathematical operations, simplifying the matching process while improving accuracy through quantitative comparison metrics.
3Loss of information
If the system processes multiple video scenes and encodes all descriptors, then retrieval completeness improves, but processing time increases
Solution Approach 1:
The system performs preliminary encoding of all video scene descriptors into vectors before actual search operations. This one-time pre-processing ensures that all information is ready for instant retrieval, achieving complete information coverage without increasing processing time during user queries, as the heavy computation is done in advance.
Data Source
AI summary
An apparatus for video searching, includes a memory storing instructions, and a processor configured to execute the instructions to split a video into scenes, obtain, from the scenes into which the video is split, one or more textual descriptors describing each of the scenes, encode the obtained one or more textual descriptors describing each of the scenes into a video scene vector of each of the scenes, encode a user query into a query vector having a same semantic representation as that of the video scene vector of each of the scenes into which the one or more textual descriptors describing each of the scenes are encoded, and identify whether the video scene vector of at least one among the scenes corresponds to the query vector into which the user query is encoded.