Representative Image Selection for Trained Model Switching
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Solution Overview
Problem
Users face inefficiencies in selecting a suitable trained model for computer vision tasks due to the lack of guidelines for image selection, leading to ineffective comparisons and model selection processes.
Innovation Solution
An information processing apparatus that selects a representative image based on user operations during image capture and evaluates multiple trained models using the selected image to change the model accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comparison is performed with many images or images with which comparison is difficult, then the selection process becomes more thorough, but the efficiency of trained model selection deteriorates
Solution Approach 1:
The patent extracts only the necessary evaluation elements from the comparison process. Instead of comparing all images or difficult-to-compare images, the system extracts specific image regions (regions of interest) that are most relevant for evaluating trained model performance. This extraction approach maintains selection accuracy while significantly improving selection efficiency by reducing the comparison scope to essential elements only.
2Adaptability or versatility
If a user creates a trained model in accordance with their purpose, then the model fits the user's needs, but the complexity of the selection process increases
Solution Approach 1:
The patent introduces an intermediary evaluation mechanism that mediates between the user's specific needs and the available trained models. The system uses extracted image regions as intermediaries to objectively evaluate how well each trained model meets the user's purpose. This intermediary approach maintains model suitability while reducing selection process complexity by providing a standardized evaluation framework that automatically assesses model performance against user requirements.
Data Source
AI summary
An information processing apparatus comprises a selection unit configured to select a representative image, from among images captured by an imaging unit configured to perform processing in which a trained model is used on a captured image, in accordance with a user operation performed by a user in the capturing, and a change unit configured to evaluate a plurality of trained models by using the representative image selected by the selection unit and change a trained model to be used by the imaging unit based on a result of the evaluation.