Comic Panel Annotation Using Local and Global Feature Tracking
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Solution Overview
Problem
Existing automated systems struggle to efficiently annotate and index comics, graphic novels, and manga due to the diverse drawing styles and changing features of characters over time, making manual tagging labor-intensive and impractical.
Innovation Solution
A system utilizing machine learning models to identify and track local and global features within comics panels, including edge detection, multi-quality tracking, and scene segmentation, to automate the annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tagging systems are used to annotate visual media content, then productivity and efficiency are improved, but the system fails to accurately recognize characters with diverse drawing styles and changing features
Solution Approach 1:
The system dynamically adapts to varying drawing styles by using multi-quality tracking that adjusts recognition parameters based on the specific characteristics of each comic panel. The machine learning models are trained to handle dynamic style variations rather than assuming static character appearances.
Solution Approach 2:
The system changes recognition parameters to accommodate different drawing qualities and styles. By adjusting detection thresholds, feature weights, and matching criteria based on the specific panel context, the system maintains high accuracy across diverse artistic representations while preserving automated efficiency.
2Measurement precision
If manual annotation is used to ensure accurate character identification across diverse styles, then measurement precision is improved, but productivity and time consumption deteriorate
Solution Approach 1:
The system performs self-correction and self-improvement by automatically learning from annotated data. The machine learning models continuously refine their recognition capabilities through training on labeled examples, enabling the system to achieve high accuracy autonomously without requiring continuous manual intervention for each new comic panel.
Solution Approach 2:
The system incorporates feedback mechanisms where annotation results are used to retrain and improve the machine learning models. This closed-loop approach allows the system to learn from errors and improve character recognition accuracy across different drawing styles over time, maintaining high precision while preserving automated processing speed.
3Device complexity
If the automated system is trained on a subset of drawing styles, then device complexity and training time are reduced, but adaptability to recognize all character styles deteriorates
Solution Approach 1:
The system employs universal machine learning models that can handle multiple drawing styles and quality levels through a unified architecture. Rather than creating separate specialized systems for each style, the models are designed to be multi-functional, adapting to various artistic representations through learned feature extraction and pattern recognition.
Solution Approach 2:
The system performs preliminary training on diverse drawing styles during the model development phase, preparing the algorithms to handle style variations before actual annotation begins. By pre-training on comprehensive style variations and incorporating style-invariant feature extraction, the system reduces the need for extensive retraining while maintaining broad adaptability across different artistic representations.
Data Source
AI summary
A system includes a computing platform having processing hardware, and a systems memory storing a software code. The processing hardware is configured to execute the software code to receive content including an image having multiple image regions, determine boundaries of each of the image regions to identify multiple bounded image regions, identify, within each of the bounded image regions, one or more local features and one or more global features, and identify, within each of the hounded image regions, another one or more local features based on a comparison with corresponding local features identified in each of one or more other bounded image regions. The processing hardware is further configured to execute the software code to annotate each of the bounded image regions using its respective one or more local features, its other one or more local features, and its one or more global features, to provide annotated content.