Object Identification Model Using Confusable Category Rules
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Solution Overview
Problem
Current methods for object identification in computer vision are time-consuming and lack accuracy, often requiring users to manually search through search engines and dictionaries, and may yield multiple similar results without clear categorization.
Innovation Solution
A method and system that utilize a pre-established object identification model to analyze images, search a rule database for confusable categories, and provide prompt information to users on distinguishing features to improve identification accuracy, allowing users to capture additional images showcasing these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search through search engines and dictionaries is used for object identification, then users can obtain object category information, but the process is very time consuming
Solution Approach 1:
The patent replaces the mechanical manual search process with an automated image recognition system using deep learning models. The system automatically processes images through pre-trained neural networks to identify object categories, eliminating the need for manual searching through search engines and dictionaries while maintaining high identification accuracy.
Solution Approach 2:
The patent employs pre-trained deep learning models that have been previously trained on large datasets. These models are ready to perform object identification tasks without requiring real-time manual intervention or preliminary searching, thus significantly reducing the time required for object category identification.
2Productivity
If image-based object identification is used, then identification speed is improved, but multiple similar results are returned without clear categorization
Solution Approach 1:
The patent implements a feedback mechanism where the system not only returns identification results but also provides confidence scores and similarity metrics. This feedback helps users understand the reliability of each result and distinguish between similar categories, thereby maintaining categorization clarity while preserving fast image-based identification.
Solution Approach 2:
The patent segments the identification results by organizing them into hierarchical categories with confidence levels. Similar objects are grouped together with clear categorical labels and differentiation criteria, allowing users to quickly distinguish between similar results while maintaining the speed advantages of image-based identification.
3Measurement precision
If deep learning models are used for object identification, then identification accuracy is improved, but model complexity and computational resources increase
Solution Approach 1:
The patent employs pre-trained deep learning models that have been trained on diverse datasets to recognize multiple types of objects across different categories. These universal models can identify various plant species, animals, and other objects using the same underlying architecture, reducing the need for multiple specialized models and thereby managing complexity while maintaining high accuracy.
Data Source
AI summary
The present disclosure relates to a method and system for object identification. A method for object identification is provided, comprising: acquiring a first image presenting at least a part of an object from a user; identifying a category of the object through a pre-established object identification model based on the first image, to obtain at least one result; searching a rule database containing at least one set of confusable object categories using the at least one result, to determine whether there is a confusable object category corresponding to the at least one result; and if there is a confusable object category corresponding to the at least one result in the rule database, returning the at least one result and the confusable object category corresponding thereto to the user.