Deep Content Classification for Multimedia Concept Structures
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Solution Overview
Problem
Current multimedia data search and management systems face challenges in effectively representing and comparing video content due to its abstract and complex nature, leading to inefficiencies in indexing, classification, and clustering, especially when metadata is inadequate or absent, resulting in non-scalable solutions that hinder high-quality searching.
Innovation Solution
A deep-content-classification system generates concept structures by receiving multimedia data elements, querying for sub-concepts, checking logic rules, and creating new concept structures using sub-concepts that satisfy these rules, thereby producing compact representations for efficient storage, retrieval, and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If model-based methods and metadata are used to represent multimedia data, then the data can be stored and indexed, but the representation becomes too abstract and complex to adequately represent the actual content
Solution Approach 1:
The patent segments multimedia data into distinct components: visual features, audio features, and text features. Each component is processed and represented separately through feature extraction, allowing the system to maintain detailed content information while enabling efficient indexing and search operations on individual feature types.
Solution Approach 2:
The patent introduces feature vectors as intermediary representations between raw multimedia content and metadata. These feature vectors capture essential content characteristics (visual, audio, text) in a structured format that bridges the gap between complex content and searchable indices, preserving information while enabling efficient comparison.
2Reliability
If comprehensive metadata is collected to describe multimedia content, then search capability improves, but the system becomes non-scalable when handling vast amounts of data
Solution Approach 1:
The patent divides the search process into separate stages for different feature types (visual, audio, text). Each feature type is indexed and searched independently, allowing the system to scale by processing features in parallel and reducing the computational burden of comparing comprehensive metadata across all data elements.
Solution Approach 2:
The patent transforms complex multimedia content into standardized feature vectors with specific dimensions and parameters. This parameterization allows for efficient mathematical operations and comparisons, enabling scalable search algorithms that can handle large datasets while maintaining search accuracy through consistent feature representation.
3Measurement precision
If detailed content analysis is performed on each multimedia element, then matching accuracy improves, but the number of comparisons needed increases significantly
Solution Approach 1:
The patent segments the comparison process into feature-level operations. Instead of comparing entire multimedia elements, the system compares individual feature vectors (visual, audio, text) separately. This segmentation reduces the dimensionality of each comparison while maintaining overall matching accuracy through aggregation of feature-level results.
Solution Approach 2:
The patent creates compact feature vector copies that represent the essential characteristics of multimedia content. These feature vectors serve as simplified proxies that can be quickly compared and matched, reducing the time required for detailed content analysis while preserving the information needed for accurate matching through their structured representation.
Data Source
AI summary
A method and system for method for generating concept structures are disclosed. The method comprises receiving a request to create a new concept structure, wherein the request includes at least a multimedia data element (MMDE) related to the new concept structure; querying a deep-content-classification (DCC) system using the MMDE to find at least one sub-concept, wherein a sub-concept is a concept structure that partially matches the received MMDE; checking if the at least one sub-concept satisfies at least one predefined logic rule; generating one or more sub-concepts from the at least MMDE; and generating the new concept structure using one or more sub-concepts out of the at least one sub-concepts that satisfies the predefined logic rule.