Frame Predictor Error Representation for Media Feature Extraction
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Solution Overview
Problem
Feature engineering for characterizing media items, such as videos with temporal variations, is time-consuming and challenging due to the need for human selection and labeling of features, especially when identifying non-human-selected features.
Innovation Solution
A feature extraction system using a frame predictor to generate error representations based on prediction errors, which characterizes media items by capturing their differences over time, allowing for automated identification of discriminatory features without human labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human selection and labeling of features is used to characterize media items, then feature extraction accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system uses the media item's own frame sequences to automatically generate features through prediction error analysis. The frame predictor processes the media item's frames and generates error representations that serve as discriminatory features, eliminating the need for external human annotation while maintaining high accuracy in characterizing temporal variations.
Solution Approach 2:
The patent replaces the mechanical process of human feature selection and labeling with an automated computational system. The frame predictor and error representation generator substitute human experts, using machine learning models to automatically identify and extract discriminatory features from media items based on prediction errors between consecutive frames.
2Measurement precision
If human selection and labeling of features is used to characterize media items, then feature quality is improved, but cost increases
Solution Approach 1:
The system enables media items to self-characterize by generating their own features through the frame predictor's error analysis. This self-service approach eliminates expensive human expert involvement in feature selection and labeling, significantly reducing costs while maintaining high feature quality through automated prediction error-based feature extraction.
Solution Approach 2:
The patent substitutes expensive human expert systems with automated computational processes. The frame predictor and error representation generation replace human feature engineering, eliminating costs associated with human time and expertise while maintaining or improving feature quality through systematic automated analysis of temporal variations.
3Productivity
If automated frame prediction is used to generate error representations, then productivity is improved, but feature extraction accuracy may worsen due to lack of human insight
Solution Approach 1:
The frame predictor operates with feedback by comparing predicted frames against actual frames to generate error representations. This feedback mechanism allows the system to automatically identify discrepancies and extract discriminatory features based on prediction errors, achieving both high productivity through automation and high accuracy by capturing genuine temporal variations that differ from predicted patterns.
Solution Approach 2:
The patent replaces human expert insight with sophisticated automated error analysis. Instead of relying on human understanding of media content, the system uses machine learning-based frame prediction and error representation to automatically identify discriminatory features, achieving both efficiency and accuracy through computational analysis of temporal patterns.
Data Source
AI summary
A media item comprising a set of frames is received by a feature extraction system. A frame predictor is executed on each frame of the set of frames. An error representation is extracted for each frame of the set of frames during the execution of the frame predictor. An error-based feature vector is generated from the error representations associated with each frame of the set of frames. A seed media item is identified having a first error-based feature vector. A similarity score is determined among the first error-based feature vector and each error-based feature vector of a set of error-based feature vectors. A subset of error-based feature vectors, hence a subset of corresponding media items, is selected based on similarity score.