Hierarchical Content Analysis Tree for Multimedia Semantics
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Solution Overview
Problem
Current multimedia content analysis systems face inefficiencies in accessing, indexing, and retrieving digital multimedia content due to its bulky data volume and unstructured format, limiting non-linear access and requiring labor-intensive manual indexing, especially for large collections.
Innovation Solution
A comprehensive framework for extracting multiple-resolution semantics in composite media content analysis using a hierarchical content analysis tree that integrates various media features like audio, visual, and text to facilitate efficient access, indexing, and retrieval, applicable to composite media streams including audio, visual, embedded text, presentation, and graphics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual indexing is used to facilitate content access, then indexing accuracy is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables automated content indexing by having the multimedia analysis system itself extract and organize indexing information from the content without requiring external manual intervention. The system processes multimedia content automatically, extracting semantics, objects, events, and temporal relationships to generate indexes, thereby eliminating the labor-intensive manual indexing process while maintaining accurate content representation.
2Measurement precision
If comprehensive multimedia analysis is performed to extract content semantics, then content retrieval accuracy is improved, but system complexity increases
Solution Approach 1:
The comprehensive multimedia analysis system is divided into multiple independent analysis modules, each responsible for specific aspects such as audio analysis, visual analysis, text analysis, and temporal relationship analysis. Each module processes its designated aspect independently and outputs results that are integrated by a coordination mechanism, thereby achieving comprehensive content semantics extraction while managing system complexity through modular architecture.
Solution Approach 2:
The framework employs a unified analysis architecture that handles multiple media types (audio, visual, text) and multiple analysis dimensions (objects, events, semantics, temporal relationships) through a common processing framework. This multi-functional design allows the system to perform comprehensive analysis without requiring separate dedicated systems for each analysis type, thereby reducing overall system complexity.
3Productivity
If automated content analysis is implemented to reduce manual indexing, then productivity is improved, but analysis precision may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the analysis results from multiple modules are continuously refined and validated. The coordination mechanism receives outputs from various analysis modules, validates their consistency, and iteratively improves the extracted semantics and temporal relationships. This feedback loop ensures that automated analysis maintains high precision by continuously refining results based on cross-validation among different analysis components.
Solution Approach 2:
The system combines multiple analysis approaches and techniques into a composite analysis framework, integrating different algorithms and methods for audio, visual, and text analysis. By combining multiple analysis strategies rather than relying on a single method, the system achieves both high productivity through automation and high precision through the complementary strengths of different analysis techniques working together.
Data Source
AI summary
Disclosed is a general framework for extracting semantics from composite media content at various resolutions. Specifically, given a media stream, which may consist of various types of media modalities including audio, visual, text and graphics information, the disclosed framework describes how various types of semantics could be extracted at different levels by exploiting and integrating different media features. The output of this framework is a series of tagged (or annotated) media segments at different scales. Specifically, at the lowest resolution, the media segments are characterized in a more general and broader sense, thus they are identified at a larger scale; while at the highest resolution, the media content is more specifically analyzed, inspected and identified, which thus results in small-scaled media segments.