Multimodal Conversation Templates for Image Object Recognition
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Solution Overview
Problem
Current learning applications with physical-digital interaction face challenges in accurately detecting objects in images, particularly in noisy images with multiple objects, and fail to effectively interact with users through digital objects.
Innovation Solution
The implementation of image object recognition techniques using multimodal conversation templates, which involve a vision recognition module and an image disambiguation module leveraging a wordweb to generate questions and filter classification labels based on user input, thereby resolving ambiguity and improving object classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If object recognition is performed using automated image analysis, then productivity is improved, but measurement precision deteriorates due to ambiguity in noisy images with multiple objects
Solution Approach 1:
The system implements feedback by presenting classification results to users and incorporating their corrections or confirmations back into the system. This allows the automated system to learn from user feedback, improving accuracy over time while maintaining high productivity through initial automated classification.
Solution Approach 2:
The patent introduces an intermediary layer between automated image analysis and final classification decisions. This intermediary presents ambiguous cases to users for clarification, allowing the system to maintain high productivity for clear cases while achieving high precision through user input for ambiguous cases.
2Measurement precision
If user interaction is added to resolve classification ambiguity, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system changes the parameter of user interaction from complex free-form communication to structured multiple-choice responses. By presenting users with predefined options rather than open-ended questions, the system achieves high classification precision while keeping the interaction model simple and the device complexity low.
Solution Approach 2:
The patent segments the classification process into distinct stages: automated initial classification, ambiguity detection, targeted user questioning, and final resolution. This segmentation allows the system to apply complex processing only where necessary while keeping the overall device complexity manageable through modular design.
3Measurement precision
If multiple classification labels are considered for noisy images, then measurement precision is improved, but loss of information increases due to ambiguity
Solution Approach 1:
The system performs partial classification by focusing computational resources on resolving the most ambiguous cases rather than attempting to perfectly classify all objects. By applying excessive scrutiny only where needed (high-ambiguity cases) and accepting good-enough classifications for clear cases, the system maintains measurement precision while minimizing information loss through efficient resource allocation.
Data Source
AI summary
An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.