Semi-Supervised Character Recognition in Animated Video Indexing
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Solution Overview
Problem
Current methods for character recognition in animated videos require extensive manual annotation, which is tedious and limits scalability, as they rely on manual drawing of bounding boxes and tagging for each character in every frame, making efficient search and retrieval challenging.
Innovation Solution
A media indexer with a character recognition engine that automatically detects and groups characters in multi-frame animated media files, using keyframes to reduce computational complexity and employing a character detection module to propose bounding boxes, followed by grouping and labeling to train an image classification model for semi-supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for character recognition in animated videos, then detection accuracy can be ensured, but the process becomes tedious and scalability is severely limited
Solution Approach 1:
The system performs preliminary automatic detection to generate candidate character regions and groupings before manual annotation. This preliminary action reduces the scope of manual work from annotating every character in every frame to only verifying and correcting automatic detections, thereby maintaining accuracy while improving scalability
Solution Approach 2:
The system enables semi-supervised learning where the model trains on automatically generated annotations from keyframes and then performs self-service detection on subsequent frames. The automatic detection system serves itself by generating training data and performing detection, requiring minimal ongoing manual intervention
2Loss of information
If manual annotation is performed for each character in every frame, then comprehensive character indexing is achieved, but the time and effort required increases significantly
Solution Approach 1:
The system extracts only keyframes from the animated video for manual annotation, separating the critical training data extraction from the entire video processing. This extraction approach maintains character indexing completeness while reducing annotation time by focusing only on representative frames
Solution Approach 2:
The system performs partial annotation on keyframes rather than complete annotation on all frames. This partial action is sufficient to train the model for comprehensive character indexing, achieving complete character coverage across all frames without the proportional time investment
3Reliability
If extensive manual labeling is used for training image classification models, then model accuracy improves, but the complexity and cost of the process increases
Solution Approach 1:
The training process is segmented into automatic detection phase and manual verification phase. The automatic detection handles the bulk of data generation, while manual verification focuses only on correcting errors. This segmentation maintains model accuracy while reducing process complexity and resource requirements
Data Source
AI summary
The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.