Multimodal Feature Embeddings for Faster Multimedia Object Search
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Solution Overview
Problem
Existing multimedia data search systems face challenges in efficiently processing and searching for objects within large datasets due to the high computational requirements of semantic labeling and the time-consuming nature of dataset annotation, which are costly and resource-intensive.
Innovation Solution
The use of multimodal feature embeddings generated in a pre-output layer of a machine learning algorithm to characterize object characteristics across various data modalities, allowing for efficient object search without relying on semantic labels in the output layer, and the implementation of user-based privacy filters to customize search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic labeling is used for object search in multimedia data, then search accuracy is improved, but computational requirements and processing time increase significantly
Solution Approach 1:
The patent extracts and utilizes intermediate feature embeddings from the pre-output layer of the machine learning model, separating the search function from the complete semantic labeling process. This allows obtaining object characteristics without performing the full computationally expensive semantic labeling, thereby reducing computational requirements while maintaining search accuracy.
Solution Approach 2:
The patent performs preliminary extraction of feature embeddings during the media content processing stage, before actual search queries are executed. By pre-computing and storing these intermediate embeddings, the system avoids repeated full semantic labeling during search operations, significantly reducing processing time and computational load during query execution.
2Measurement precision
If semantic labeling is performed on large multimedia datasets, then object identification accuracy is improved, but processing time becomes excessively long
Solution Approach 1:
The patent extracts intermediate feature embeddings from the pre-output layer, obtaining sufficient object characteristics without completing the full semantic labeling pipeline. This extraction approach maintains object identification accuracy by capturing essential features while avoiding the time-consuming final labeling stages.
Solution Approach 2:
The system performs preliminary processing to generate and store feature embeddings for all media content in advance. When search queries are received, the system directly compares query embeddings against these pre-generated embeddings, eliminating the need for real-time semantic labeling and dramatically reducing processing time.
3Productivity
If detailed semantic labels are generated for all media content, then search completeness is improved, but computational resources and annotation costs increase
Solution Approach 1:
The patent extracts intermediate feature embeddings that capture essential object characteristics without generating complete semantic labels. This approach maintains search completeness by preserving sufficient information for accurate object matching while avoiding the computational overhead and annotation costs associated with generating detailed semantic labels for all content.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for searching objects within multimedia content based on multi-modal embeddings. An example method can include receiving media content including a plurality of video frames. The method can include steps for generating, using a pre-output layer of a machine learning algorithm, one or more multimodal feature embeddings describing at least one object for the plurality of video frames, receiving a query including a request to search the media content for a matching object, determining whether the media content includes the matching object based on the one or more multimodal feature embeddings describing the at least one object, and returning one or more results in response to determining that the media content includes the matching object. Systems and machine-readable media are also provided.