Multimedia Concept Structure Generation via Patch Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current multimedia data search engines face challenges in effectively managing and searching vast amounts of multimedia data due to the abstract and complex nature of video content, which cannot be adequately represented by existing metadata, leading to inefficiencies in indexing, classification, and retrieval.
Innovation Solution
A deep-content-classification system generates concept structures by creating patches from multimedia data elements, producing signatures that are clustered and reduced to form metadata, enabling efficient storage, retrieval, and matching of multimedia data, thereby overcoming the limitations of prior art systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art systems use existing metadata to represent multimedia data content, then the system is simple to operate, but the representation is inadequate and abstract, leading to poor search effectiveness
Solution Approach 1:
The patent segments multimedia data into discrete visual concepts through automated annotation, breaking down complex video content into identifiable objects, actions, and scenes that can be individually tagged and searched, thereby improving representation accuracy without requiring manual metadata creation for the entire content
Solution Approach 2:
The patent introduces automated visual concept annotation as an intermediary layer between raw multimedia data and search queries. This annotation system acts as a mediator that transforms unstructured video content into structured, searchable visual concepts, improving search effectiveness while maintaining system simplicity
2Reliability
If the system indexes and clusters all multimedia data elements, then search completeness is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent performs preliminary automated annotation of multimedia data during ingestion, creating visual concept indices in advance. This preliminary action ensures that when search queries are executed, the system can quickly retrieve pre-processed visual concepts rather than analyzing raw data in real-time, maintaining search completeness while reducing query response time
Solution Approach 2:
The patent extracts key visual concepts from multimedia data and separates them from the original content for independent indexing and storage. By extracting only the essential visual elements (objects, actions, scenes) rather than processing entire video files during search, the system maintains comprehensive search capability while significantly reducing computational overhead
3Measurement precision
If the system stores detailed visual information from multimedia data, then search accuracy is improved, but storage requirements and data processing complexity increase
Solution Approach 1:
The patent creates simplified visual concept representations (copies) of multimedia content that capture essential searchable features without storing the entire original data. These annotated visual concepts serve as lightweight proxies that maintain search accuracy while occupying minimal storage space compared to the source multimedia files
Data Source
AI summary
A system and method for generating concept structures based on a plurality of multimedia data elements (MMDEs). The method includes: generating, based on the plurality of MMDEs, a plurality of patches, wherein each patch is at least a portion of one of the MMDEs; generating, based on the plurality of patches, a plurality of signatures for the plurality of MMDEs; clustering the generated plurality of signatures into a plurality of clusters; generating metadata for each of the plurality of clusters; and creating, based on the plurality of clusters, at least one concept structure, wherein each concept structure includes at least one of the plurality of clusters and the metadata associated with the at least one of the plurality of clusters.