Bottle Image Segmentation for Fine-Grained Visual Attribute Extraction
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Solution Overview
Problem
Existing image recognition systems fail to provide sufficient detailed features for beverage bottles to support occasion brand-price-packaging-channel analysis, particularly in identifying fine-grained characteristics such as shape, color, and design elements.
Innovation Solution
A computer-implemented method using a trained segmentation model to segment objects into multiple parts, followed by attribute extraction models to analyze shape, color, and design elements, employing a modified Mask-RCNN model for instance segmentation and unsupervised CLIP model for color analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is used for image recognition, then the system complexity is low, but the extraction of fine-grained attributes is insufficient
Solution Approach 1:
The patent segments the image processing task into multiple specialized models: a segmentation model that divides the beverage container into distinct regions (cap, body, label, bottom), and multiple attribute extraction models that analyze different visual attributes (color, shape, design elements) separately. This segmentation enables each model to focus on specific features, improving overall attribute extraction precision while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent creates a multi-functional system where the segmentation model serves as a foundation for multiple attribute extraction models. The same segmented regions are used across different analysis tasks (color analysis, shape analysis, design element analysis), allowing the system to extract diverse fine-grained attributes from a single segmentation pass, thereby improving measurement precision without proportionally increasing complexity.
2Measurement precision
If detailed segmentation is performed on beverage bottles, then fine-grained attribute extraction is improved, but processing time increases
Solution Approach 1:
The segmentation model performs preliminary action by dividing the beverage container into meaningful regions before attribute extraction begins. This pre-segmentation creates structured regions of interest that guide subsequent attribute analysis, allowing the system to focus computational resources on extracting attributes from already-identified regions rather than analyzing the entire image uniformly, thus improving feature extraction accuracy while optimizing processing time.
Solution Approach 2:
By segmenting the beverage container into distinct anatomical regions (cap, body, label, bottom), the system enables parallel processing of different attributes across different regions. This segmentation strategy allows multiple attribute extraction operations to be performed simultaneously on separate regions, improving overall processing efficiency while maintaining high feature extraction accuracy through region-specific analysis.
3Adaptability or versatility
If multiple attribute extraction models are used, then comprehensive packaging analysis is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal segmentation model that serves as a common foundation for multiple specialized attribute extraction models. The segmented regions produced by the single segmentation model are reused across color analysis, shape analysis, and design element analysis, creating a multi-functional system where one segmentation pass supports diverse analytical tasks. This approach achieves comprehensive packaging analysis while controlling system complexity through shared infrastructure.
Solution Approach 2:
The system segments both the image processing task and the beverage container structure. By dividing the container into standard regions (cap, body, label, bottom) and dividing the analysis into specialized models, the patent creates a modular segmented architecture. This segmentation enables comprehensive analysis across multiple attributes while managing complexity through clear separation of concerns and reusable modular components.
Data Source
AI summary
A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.