Semantic Image Segmentation Using Graph Cut Energy Minimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image segmentation techniques face challenges in accurately selecting objects from digital images, particularly in highly textured areas, often requiring tedious user interaction and struggling with trade-offs between ease of use and segmentation accuracy, while fully automated methods lack responsiveness to user input.
Innovation Solution
The technique enables digital image segmentation using semantic input, allowing users to specify objects or regions through language-based queries, which queries a database to gather positive and negative exemplars, validate object proposals, and apply a location prior and appearance models to an iterative graph cut energy minimization framework for precise segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fully automated segmentation methods are used, then ease of operation is improved, but responsiveness to user input deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions (clicks, selections) are continuously processed to refine and update segmentation results. The segmentation model receives feedback from user actions and adjusts its predictions accordingly, maintaining both automation and responsiveness to user intent.
Solution Approach 2:
The segmentation system dynamically adapts to user input by adjusting its parameters and focus in real-time. When users interact with the image or provide feedback, the system dynamically recalculates segmentation boundaries and object proposals, transitioning from static automated segmentation to dynamic user-responsive segmentation.
2Measurement precision
If traditional segmentation tools are used, then ease of operation deteriorates due to tedious user interaction, but measurement precision can be maintained
Solution Approach 1:
The system performs self-service segmentation by automatically generating object proposals, identifying boundaries, and segmenting regions without requiring manual user interaction. The AI model independently analyzes the image, generates segmentation hypotheses, and produces results, eliminating the need for tedious manual operations while maintaining high precision.
Solution Approach 2:
The patent replaces manual mechanical interaction (mouse clicks, brush strokes, region drawing) with an automated computational system. Instead of users manually defining regions through mechanical actions, the system uses computer vision algorithms and machine learning models to automatically detect and segment objects, substituting human manual operations with automated intelligent processing.
3Productivity
If automated methods process entire images, then productivity is improved, but measurement precision deteriorates in highly textured areas
Solution Approach 1:
The system applies segmentation at multiple levels: first dividing the image into candidate object regions, then further segmenting those regions into precise boundaries. This hierarchical segmentation approach allows the system to focus computational resources on relevant areas, improving both processing efficiency and segmentation accuracy in complex textured regions.
Solution Approach 2:
The system applies different processing strategies to different regions of the image based on local characteristics. In highly textured areas, the model adjusts its analysis parameters and applies more sophisticated boundary detection algorithms, while in uniform regions it uses simpler methods, optimizing both speed and precision for each local area.
Data Source
AI summary
Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.