Automated Image Analysis and Indexing for Visual Media
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Solution Overview
Problem
Automated systems struggle to efficiently analyze and index visual media content like comics, graphic novels, and manga due to varying drawing styles and qualities over time, making manual processing necessary for accurate search indexing.
Innovation Solution
A system utilizing machine learning models to identify and annotate bounded image regions and sub-regions within visual media content, employing edge detection, saliency detection, and computer vision to extract local and global features, reducing the need for manual processing and improving search relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used for image analysis and indexing, then processing speed and efficiency are improved, but accuracy and recognition reliability deteriorate due to varying drawing styles
Solution Approach 1:
The system segments the image processing task into multiple specialized modules: edge detection module, saliency detection module, and feature extraction module. Each module handles specific aspects of the varying drawing styles, allowing the system to maintain high processing speed while improving recognition accuracy through specialized handling of different visual characteristics
Solution Approach 2:
The system dynamically adjusts processing parameters based on detected drawing style characteristics. By changing parameters such as edge detection thresholds, saliency weights, and feature extraction settings according to the specific drawing style detected, the system maintains high accuracy across diverse comic styles while preserving automated processing efficiency
2Measurement precision
If manual processing is used for accurate annotation, then indexing accuracy is improved, but processing time and labor costs increase
Solution Approach 1:
The system performs self-correction and self-optimization by automatically learning from detected drawing styles and adjusting its processing parameters accordingly. The automated system serves itself by dynamically adapting to maintain high accuracy without human intervention, eliminating the need for manual processing while preserving indexing precision
Solution Approach 2:
The system replaces the mechanical manual annotation process with an automated computer vision system that uses edge detection, saliency detection, and feature extraction algorithms. This substitution maintains high indexing accuracy while dramatically reducing processing time and eliminating labor costs associated with manual annotation
3Device complexity
If automated systems recognize only a subset of drawing styles, then system complexity is reduced, but adaptability and coverage deteriorate
Solution Approach 1:
The system achieves multi-functionality by integrating multiple detection and extraction modules that can handle various drawing styles within a single unified framework. The edge detection, saliency detection, and feature extraction modules work together to provide universal coverage across different comic styles without requiring separate specialized systems, maintaining manageable complexity while expanding adaptability
Data Source
AI summary
A system includes a computing platform having processing hardware, and a memory storing software code. The processing hardware is configured to execute the software code to receive an image having a plurality of image regions, determine a boundary of each of the image regions to identify a plurality of bounded image regions, and identify, within each of the bounded image regions, one or more image sub-regions to identify a plurality of image sub-regions. The processing hardware is further configured to execute the software code to identify, within each of the bounded image regions, one or more first features, respectively, identify, within each of the image sub-regions, one or more second features, respectively, and provided an annotated image by annotating each of the bounded image regions using the respective first features and annotating each of the image sub-regions using the respective second features.