Classifier Selection for Accuracy and Execution Time Trade-offs
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Solution Overview
Problem
Existing image classification systems face a trade-off between identification accuracy and execution time, lacking a mechanism to adjust both simultaneously, which can lead to suboptimal resource usage and detection errors.
Innovation Solution
An information processing apparatus that selects a classifier based on predetermined conditions for both identification accuracy and execution time, using test images to measure and adjust these parameters, allowing for the selection of the most suitable classifier for specific needs, thereby optimizing resource usage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If identification accuracy is increased to reduce detection errors, then detection precision is improved, but execution time increases and resource consumption increases
Solution Approach 1:
The patent applies dynamics by making the classifier selection adjustable and adaptable to different operational requirements. The system dynamically selects among multiple classifiers with varying accuracy-performance characteristics based on real-time or configurable needs, allowing the identification apparatus to transition between high-accuracy mode and fast-execution mode as required
Solution Approach 2:
The patent changes the parameter of classifier selection criteria from fixed to variable. By establishing selection criteria that consider both identification accuracy and execution time, the system can adjust which classifier is deployed based on the desired balance between these two parameters, enabling flexible optimization for different应用场景
2Measurement precision
If identification accuracy is increased to reduce detection errors, then detection precision is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts resource allocation by selecting different classifiers based on operational requirements. When high accuracy is needed, a more computationally intensive classifier can be selected; when resource conservation is prioritized, a lighter classifier is chosen, making resource consumption adaptable rather than fixed
Solution Approach 2:
The patent introduces a multi-dimensional selection criterion that includes both identification accuracy and execution time (which correlates with resource consumption). This allows the system to optimize the balance between accuracy and resource usage by selecting the classifier that best meets the desired parameter combination
3Productivity
If execution time is reduced to save CPU and memory resources, then resource efficiency is improved, but identification accuracy decreases
Solution Approach 1:
The patent segments the classifier population into multiple categories with different accuracy-performance characteristics. Instead of using a single monolithic classifier, the system divides the classification task into selectable options, allowing the identification apparatus to choose the appropriate segment (classifier) based on current operational priorities
Solution Approach 2:
The selection criteria parameters are changed from single-dimensional to multi-dimensional, incorporating both identification accuracy and execution time. This enables the system to find optimal classifiers that satisfy both speed and accuracy requirements simultaneously, rather than forcing a trade-off
4Device complexity
If a single classifier is used for all cases, then device complexity is reduced, but adaptability to different user needs decreases
Solution Approach 1:
The patent implements universality by designing a classifier selection mechanism that can handle multiple different scenarios with a single system. The identification apparatus universally supports various classifier types and selection criteria, making it adaptable to different user needs (high accuracy, fast execution, resource efficiency) without requiring separate specialized systems for each case
Solution Approach 2:
The system allows dynamic changing of selection parameters to adapt to different operational contexts. By modifying the criteria used to select classifiers (emphasizing accuracy, speed, or resource usage based on needs), the system achieves high adaptability while maintaining a unified classifier management framework
Data Source
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AI summary
There is provided an information processing apparatus comprising: a storage unit configured to store a plurality of classifiers that identify an object, the classifiers having different characteristics; a measurement unit configured to measure identification accuracy and execution time of each of the plurality of classifiers for a specific object; an output unit configured to output the identification accuracy and the execution time of each of the plurality of classifiers; a selection unit configured to select, from the classifiers whose identification accuracy measured by the measurement unit meets a first condition, a classifier whose execution time meets a second condition; and a setting unit configured to perform setting to cause the selected classifier to operate on an identification apparatus.