Object Tagging System with Hierarchical Face Detection
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Solution Overview
Problem
Existing face detection technologies face limitations in effectively managing and processing detected faces, including imperfect detection and false alarms, and lack efficient methods for tagging and organizing objects in images.
Innovation Solution
A system and process for detecting, identifying, and managing objects in images, which includes automatic and manual detection, object tagging, normalization, and hierarchical organization, using techniques like Eigenfaces, Adaboost, and neural networks, with a system comprising an object detector, identifier, and manager to handle detected objects, including face detection and identification, and providing interfaces for tagging and displaying objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic detection techniques are used to detect objects in images, then detection speed and automation are improved, but false alarms and imperfect detection increase
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously evaluated and used to refine future detections. The object manager uses detection confidence scores and feedback from manual corrections to improve the reliability of automatic detection over time, reducing false alarms while maintaining automation.
Solution Approach 2:
The patent introduces an intermediary layer between automatic detection and final results - the object manager that coordinates multiple detection techniques and manages object information. This intermediary processes detection outputs, resolves conflicts between different detection methods, and provides a unified reliable result.
2Reliability
If multiple detection techniques are coordinated to improve accuracy, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple detection techniques (face detection, object detection, pattern recognition) into a unified system managed by the object manager. By combining these techniques and managing them through a single coordination layer, the system achieves improved detection reliability without proportionally increasing complexity, as the manager consolidates control.
Solution Approach 2:
The object manager serves multiple functions simultaneously - it coordinates detection techniques, manages object information, handles tagging, and provides user interface control. This multi-functionality reduces overall system complexity by consolidating what could be separate complex subsystems into a single universal manager.
3Measurement precision
If manual tagging and organization methods are used, then tagging accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automatic tagging based on detection results and object characteristics before allowing manual refinement. This preliminary action provides a head start on accurate tagging, reducing the time needed for manual processing while maintaining high accuracy through subsequent manual verification when needed.
Solution Approach 2:
The system enables self-service tagging where the detection system automatically generates tag suggestions and organizational structure based on detected objects and their characteristics. Users can accept these automatic suggestions with minimal intervention, achieving high tagging accuracy without extensive manual processing time.
4Productivity
If normalization and hierarchical organization are implemented, then object identification efficiency is improved, but computational requirements increase
Solution Approach 1:
The patent segments the object management system into hierarchical levels - from general object categories to specific identified objects. This segmentation allows efficient identification by navigating through organized layers rather than processing all objects uniformly, improving productivity while managing computational requirements through structured division of work.
Data Source
AI summary
Indicating a tag is disclosed. Indicating includes receiving an object that has been automatically detected from visual data, receiving a tag associated with the object, and indicating the tag with the visual data.