Multi-Model Object Segmentation Framework for Digital Images
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Solution Overview
Problem
Conventional digital image editing systems face challenges with accuracy, efficiency, and flexibility in object segmentation, particularly failing to accurately segment background or conceptual objects, and requiring manual intervention, which leads to imprecise and computationally inefficient results.
Innovation Solution
A multi-model object segmentation framework that combines multiple machine-learning models to detect and refine objects in digital images, merging overlapping masks and utilizing specialist models to generate accurate and refined object masks, ensuring all objects, including background and unclassified regions, are segmented automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-model object segmentation is used, then the system is simple to implement, but it fails to accurately segment background or conceptual objects and requires manual intervention
Solution Approach 1:
The patent divides the object segmentation task into multiple specialized models, each trained to detect specific types of objects (e.g., natural objects, artificial objects, text, backgrounds). This segmentation of functionality allows each model to specialize in particular object categories, improving overall segmentation accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system creates a universal segmentation framework that combines multiple specialized models into a single integrated system. This multi-functional approach enables the system to handle diverse object types (natural objects, artificial objects, text, backgrounds) that a single conventional model cannot accurately segment, achieving comprehensive object detection across multiple categories
2Productivity
If manual object selection is used, then the system can handle any object type, but it leads to computational inefficiencies and imprecise results
Solution Approach 1:
The system performs preliminary automated object detection and segmentation before any user interaction is needed. By pre-processing the image to identify and segment all objects using multiple specialized models, the system prepares accurate object masks in advance, eliminating the need for manual selection while maintaining high precision through the combined output of multiple detection models
3Measurement precision
If multiple object segmentation models are combined, then segmentation accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the processing workflow into parallel independent model executions followed by a consolidation phase. Multiple specialized models process the image simultaneously rather than sequentially, and their results are merged through a unified object mask generation process. This parallel processing approach maintains high detection accuracy while reducing overall processing time compared to sequential model execution
Data Source
AI summary
The present disclosure relates to a multi-model object segmentation system that provides a multi-model object segmentation framework for automatically segmenting objects in digital images. In one or more implementations, the multi-model object segmentation system utilizes different types of object segmentation models to determine a comprehensive set of object masks for a digital image. In various implementations, the multi-model object segmentation system further improves and refines object masks in the set of object masks utilizing specialized object segmentation models, which results in more improved accuracy and precision with respect to object selection within the digital image. Further, in some implementations, the multi-model object segmentation system generates object masks for portions of a digital image otherwise not captured by various object segmentation models.