Image Classification System Using Segmentation and AI Labeling
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Solution Overview
Problem
Existing image classification systems face challenges in accurately and efficiently classifying images with multiple attributes, assigning multiple information labels, and providing up-to-date text descriptions, especially in presenting the most relevant words for modern business applications.
Innovation Solution
A system comprising a central processing module, input module, storage module, segmentation module, artificial intelligence module, and string module, where the artificial intelligence module uses machine learning to build a classification model, segment images, and assign labels and text descriptions based on correlation scores and a string network, with optional character recognition for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image classification methods are used, then the system is simpler, but the classification accuracy and speed for multi-attribute images deteriorates
Solution Approach 1:
The patent segments the image classification process into multiple specialized modules: an artificial intelligence module for overall classification, a segmentation module for identifying image parts, and a character recognition module for text extraction. This segmentation allows each module to focus on specific tasks, improving overall classification accuracy for multi-attribute images while maintaining manageable system complexity through modular design.
2Loss of information
If multiple label information is assigned to products, then the information completeness improves, but the processing time and complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-segmenting images into relevant parts and pre-extracting text information before the main classification process. The segmentation module identifies and segments different parts of the image in advance, and the character recognition module extracts text information beforehand. This preliminary processing enables the artificial intelligence module to quickly assign multiple accurate labels without significantly increasing overall processing time.
3Stability of the object's composition
If outdated terms are used in label information, then the system maintains consistency with historical data, but the relevance to modern business applications deteriorates
Solution Approach 1:
The patent implements a dynamic label assignment system where the artificial intelligence module can assign both traditional and modern labels based on the classified image content. The system dynamically selects and assigns labels that are relevant to current business applications while maintaining the ability to reference historical classification standards, thus adapting to modern requirements without completely discarding historical consistency.
Data Source
AI summary
Method and system for classifying and labeling images, which can perform segmentation based on features of each part of images, classify and match the image and the segmented image based on a classification model built by the machine learning method. Meanwhile, each image is assigned with labels and text descriptions. The system also includes a string module assigning the image with a plurality of matching labels and text descriptions that are the most relevant in recent times. Furthermore, the classification model is trained by machine learning method such as an unsupervised learning, a self-supervised learning, or a heuristic algorithms. In addition, a character recognition module is provided to extract characters in the image for comprehensive learning and calculations to facilitate classification and labeling of the image.