Multimodal Video Retrieval Using Embedding Similarity
Solution Overview
Problem
The increasing volume of video content makes manual annotation and labeling cumbersome, leading to inconsistent and incomplete metadata, which hampers efficient retrieval and identification of video content, and affects compliance, recommendation systems, and advertising.
Innovation Solution
A multimodal video content classification system that uses machine learning models to automatically identify and label scene-level data by integrating text, audio, and video inputs, enabling efficient retrieval and filtering of video content based on specific attributes, reducing the need for manual annotation and increasing the accuracy of metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and labeling of video content is performed, then content understanding and metadata accuracy are improved, but productivity and efficiency deteriorate due to the increasing volume of video content
Solution Approach 1:
The patent replaces manual mechanical annotation processes with automated machine learning models including classification models and content-based image retrieval models. These systems automatically generate metadata, labels, and scene-level data from video content without human intervention, eliminating the bottleneck of manual labeling while maintaining or improving accuracy through algorithmic analysis.
Solution Approach 2:
The system enables video content to be self-annotated through automated analysis. The classification models and image retrieval models process video data independently, generating their own metadata and labels without requiring external human annotators. This self-service capability scales efficiently with increasing video volumes.
2Reliability
If manual annotation processes are used, then content labeling is performed, but loss of time increases due to operators watching the entirety of each title
Solution Approach 1:
The patent extracts key scene-level data and visual features from video content using content-based image retrieval models and classification models. Instead of requiring operators to watch entire titles, the system extracts essential visual information from representative frames and scenes, generating accurate metadata in a fraction of the time while maintaining labeling completeness.
Solution Approach 2:
The system performs preliminary automated analysis of video content to generate initial metadata and labels before any human review. The classification models pre-process video data, identifying scenes, objects, and actions, which can then be used directly or refined with minimal human intervention, dramatically reducing the time operators would otherwise spend watching and annotating content.
3Ease of operation
If keyword-based searching is used, then video retrieval is simplified, but measurement precision deteriorates due to reliance on accurate labeling
Solution Approach 1:
The patent implements a multi-functional retrieval system that combines keyword-based searching with content-based image retrieval and classification model results. The system can search using both text keywords and visual content features, providing multiple pathways to retrieve relevant video content. This universal approach maintains the simplicity of keyword search while adding the precision of visual analysis.
Solution Approach 2:
The system introduces content-based image retrieval models as an intermediary between user search queries and video content. These models translate visual search criteria into accurate video retrievals, bridging the gap between simple keyword input and precise content matching. The intermediary enables users to search by visual characteristics without requiring complex query formulations.
Data Source
AI summary
Systems and techniques for retrieving video data associated with a selected attribute are described. The systems and techniques include receiving a multimodal input associated with the attribute for querying a catalog of video data to identify video data including the attribute. A first embedding is determined for the input using a first encoder to map the input to a representation space. A second embedding is determined for the video data to map the video data to the representation space. A similarity score is determined between the video data and the input based on a distance between the embeddings. The video data associated with the attribute may be selected based on the similarity score.