Multimedia Context Identification via Signature Analysis
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Solution Overview
Problem
Current techniques fail to accurately determine the context of dynamic multimedia content in web pages, particularly missing the granularity to identify current topics or themes, and lack solutions for editing and filtering such content.
Innovation Solution
A method and system that generate signatures for multimedia content elements, analyze these signatures to determine context, and provide contextual filters, enabling the identification and editing of multimedia content context within web pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional context determination techniques (domain name mapping, textual analysis) are used, then the implementation is simple and efficient, but the context identification lacks granularity and cannot accurately reflect dynamic multimedia content
Solution Approach 1:
The patent segments multimedia content into discrete elements (images, videos, audio clips) and analyzes each element's context separately through signature generation. This allows granular context identification at the element level rather than treating the entire webpage as a single unit, thereby improving measurement precision without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary action by generating signatures for multimedia content elements in advance and storing them in a database. This pre-processing enables rapid context determination when needed, improving both accuracy and efficiency without adding complexity to the real-time analysis process.
2Adaptability or versatility
If static web page analysis is used, then the processing is straightforward, but it cannot provide current context of dynamically changed web pages
Solution Approach 1:
The system generates and stores signatures for multimedia content elements in advance, creating a ready-to-use reference database. When dynamic content changes occur, the pre-computed signatures enable rapid context determination without requiring full re-analysis, thus improving adaptability while minimizing time loss.
Solution Approach 2:
The patent creates signature copies of multimedia content elements that can be quickly compared and matched. These signature copies serve as lightweight representations that enable fast adaptation to dynamic content changes without processing the actual large-sized multimedia files, reducing time loss while maintaining versatility.
3Measurement precision
If high-level context analysis (e.g., news category) is used, then the classification is broad and useful, but it lacks specific topic granularity (e.g., election of particular candidate)
Solution Approach 1:
The patent segments context analysis into multiple hierarchical levels: first determining broad category context, then analyzing specific multimedia elements for detailed topic context. This segmentation allows the system to provide both high-level classification and granular topic identification without overwhelming information processing load, as each level processes only relevant data.
Solution Approach 2:
The system applies local quality by providing different levels of context granularity at different locations in the system. The overall webpage receives high-level context classification, while individual multimedia content elements receive detailed topic-level analysis. This differentiated approach maximizes information utility without proportionally increasing processing load across the entire system.
Data Source
AI summary
A method and system for providing contextual filters respective of an identified context of a plurality of multimedia content elements are provided. The method comprises receiving the plurality of multimedia content elements; generating at least one signature for each of the plurality of multimedia content elements; determining a context of each of the plurality of multimedia content elements based on its respective at least one signature, wherein a context is determined as the correlation among a plurality of cluster of signatures; and providing at least one contextual filter respective of the context of each of the plurality of multimedia content elements.