Multimedia Content Indexing via Client-Side Feature Segmentation
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Solution Overview
Problem
Traditional search and retrieval techniques are inadequate for indexing multimedia content, as they primarily focus on textual metadata and ignore the primary content of multimedia files, and are not adapted to leverage device-specific data in a distributed content environment.
Innovation Solution
A method and system for indexing multimedia content that involves segmenting multimedia files into segments, identifying features, correlating them with keywords, calculating relevance factors based on client device characteristics, and transmitting structure-searchable data to an indexing server for creating a content index, which updates a group-specific index using a weighting scheme that includes device-specific weights and confidence measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines use textual metadata only for indexing, then the indexing process is simple and fast, but the search results are inaccurate and incomplete for multimedia content
Solution Approach 1:
The multimedia content is divided into segments, and each segment is processed independently to extract features. This segmentation allows the system to handle complex multimedia content in manageable units, improving search accuracy without overwhelming the indexing process
Solution Approach 2:
Structure-searchable data (SSD) acts as an intermediary layer between the original multimedia content and the search engine. The SSD extracts and structures key features from multimedia files, enabling accurate search without requiring the search engine to directly process complex multimedia formats
2Adaptability or versatility
If search engines process all multimedia content centrally, then the index is comprehensive, but the system cannot leverage device-specific data and becomes less adaptable
Solution Approach 1:
The system assigns different weights to features based on their source device and local context. Each device's characteristics, user preferences, and content library influence the weighting of specific features, allowing the index to adapt to local qualities while maintaining overall completeness
Solution Approach 2:
The system pre-calculates device-specific weights and characteristics before performing searches. By preparing device profiles and feature weightings in advance, the system can quickly adapt search results to each device's specific context without losing comprehensive content coverage
3Measurement precision
If the system calculates relevance factors based on multiple device characteristics, then search personalization improves, but the computational overhead increases
Solution Approach 1:
The system calculates relevance factors for only the most important features based on device characteristics, rather than processing all possible features equally. This partial action approach maintains high relevance accuracy for key features while reducing overall computational energy consumption
Data Source
AI summary
A method and client device is disclosed for indexing content of a multimedia file. The method comprises using a client device to segment the content of the multimedia file into a plurality of segments and to determine structure-searchable data for each segment. Determining structure searchable data for a segment comprises (1) identifying one or more features of respective multimedia types in the segment; (2) correlating each of the identified features to one or more respective keywords; and (3) calculating one or more respective relevance factors for each of the keywords, where at least one of the relevance factors is based on one or more characteristics of the client device. The method also comprises the client device transmitting the structure-searchable data (including the keywords, relevance factors, and respective media types of the identified features) to an indexing server.