Neural Network Orchestration for Multimedia Classification Accuracy
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Solution Overview
Problem
Businesses face challenges in processing and analyzing big data, particularly due to the vast amount of unstructured and semi-structured data that conventional methods struggle to handle, leading to incomplete insights and inefficient data utilization.
Innovation Solution
The implementation of a system and method that utilizes multiple neural networks to classify media files by receiving classification results from one network, identifying segments with low confidence, and selecting alternative networks to re-classify these segments based on related data types, such as audio, video, or metadata, to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single classification neural network is used to process all media file segments, then the system complexity is low, but the classification accuracy for diverse data types deteriorates
Solution Approach 1:
The patent segments the classification task by dividing media file segments into different groups based on data types (e.g., audio, video, text). Each segment group is processed by a specialized classification neural network trained for that specific data type, thereby improving classification accuracy for each category while managing system complexity through modular organization.
Solution Approach 2:
The system implements a multi-functional classification architecture where multiple classification neural networks work together to handle diverse data types. Each network specializes in a particular data type but collectively they provide universal coverage for all media file segments, achieving both accuracy and versatility.
2Measurement precision
If multiple classification neural networks are used to process different data types, then the classification accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary classification by first identifying the data type of each media file segment and routing it to the appropriate specialized neural network before classification. This pre-processing step prevents unnecessary processing by mismatched networks and reduces overall processing time while maintaining high accuracy.
Solution Approach 2:
Each classification neural network is optimized with local quality specific to its designated data type (e.g., audio-specific features, video-specific features). This specialization allows each network to process its assigned segments efficiently and accurately, reducing processing time compared to a general-purpose network attempting to handle all types.
3Reliability
If all media file segments are processed by the same neural network, then the ease of operation is high, but the reliability of classification results deteriorates
Solution Approach 1:
The patent introduces an intermediary component that routes media file segments to the appropriate classification neural network based on data type identification. This intermediary layer manages the complexity of multiple specialized networks while maintaining ease of operation, as the routing is automated and transparent to the user.
Data Source
AI summary
Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods includes receiving, from a first transcription engine, one or more transcription results of one or more audio segments of the multimedia file; identifying a first transcription result for a first audio segment having a low confidence of accuracy; identifying a first image data of the multimedia file corresponding to the first segment; receiving, from an image classification engine trained to classify image data, an image classification result of one or more portions of the first image data in response to requesting the image classification engine to classify the first image data; and selecting, based at least on the image classification result of the one or more portions of the first image data, a second transcription engine to re-classify the first audio segment.