Object-Based Image Layer Generation Using ML Classification
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Solution Overview
Problem
Conventional digital image editing systems are inaccurate, inefficient, and inflexible in generating and managing image layers, often requiring extensive user interaction and failing to adaptively share layers across different projects.
Innovation Solution
A machine learning approach that utilizes image segmentation and object classification models to automatically generate and label image layers based on detected objects within digital images, enabling accurate, efficient, and flexible management of layers across projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use default names or labels for image layers, then the system complexity is reduced, but the accuracy of layer representation deteriorates
Solution Approach 1:
The system automatically generates descriptive layer names by detecting objects within image layers and generating names based on the detected objects. This self-service approach eliminates the need for manual naming while providing accurate, descriptive labels that reflect the actual content of each layer.
2Ease of operation
If conventional systems require extensive user interactions to generate image layers, then the ease of operation is improved, but the productivity deteriorates
Solution Approach 1:
The system performs preliminary object detection and layer generation automatically before user interaction is needed. By pre-processing the image to detect objects and create layers with descriptive names, the system eliminates the need for users to manually create layers or name them, thereby improving both ease of operation and productivity.
3Device complexity
If conventional systems fix image layers within individual projects, then the device complexity is reduced, but the adaptability deteriorates
Solution Approach 1:
The system creates a universal layer management structure where layers generated in one project can be reused in other projects. By detecting objects and generating standardized layers with descriptive names, the system enables layers to serve multiple functions across different projects, improving adaptability without significantly increasing complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating image layers and determining layer labels utilizing a machine learning approach. For example, the disclosed systems utilize an image segmentation machine learning model to segment the digital image and identify individual objects depicted within the digital image. Additionally, in some embodiments, the disclosed systems determine object classifications for the depicted objects by utilizing an object classification machine learning model. In some cases, the disclosed systems further generate image layers for the digital image by generating a separate layer for each identified object (or for groups of similar objects). In certain embodiments, the disclosed systems also determine layer labels for the image layers according to the object classifications of the respective objects depicted in each of the image layers.