Object-Based Image Editing via Semantic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image editing systems require users to understand complex instructions and manually select pixels to edit images, which can be tedious and prone to errors, especially when editing multiple objects with similar characteristics.
Innovation Solution
An object-based editing system that segments images into logical objects, assigns classifications, and allows users to select and edit objects by their semantic names, using object recognition algorithms to identify and isolate objects for editing, reducing manual labor and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel-based editing is used, then editing precision can be achieved, but operation complexity and time consumption increase significantly
Solution Approach 1:
The image is segmented into distinct object regions using automated object recognition algorithms. Each object is identified and isolated as a separate editable entity, allowing users to edit entire objects with simple selections rather than manually manipulating individual pixels. This segmentation approach maintains editing precision while dramatically reducing operational complexity.
2Ease of operation
If automated object recognition is used, then ease of operation improves, but measurement precision of object boundaries may deteriorate
Solution Approach 1:
The system provides visual feedback by displaying detected object boundaries and allowing users to review and adjust the automated segmentation results. Users can interactively refine object selections by adding or removing regions, ensuring that the final object boundaries achieve the required precision while maintaining the ease of automated operation.
3Manufacturing precision
If traditional pixel-based selection methods are used, then editing control is precise, but productivity decreases due to tedious manual operations
Solution Approach 1:
The system performs automated object identification and segmentation without requiring manual pixel-by-pixel selection. The object recognition algorithms automatically detect and isolate objects of interest, enabling users to edit multiple objects rapidly by simply selecting from the automatically generated object list, thereby significantly improving editing productivity while maintaining sufficient precision.
4Adaptability or versatility
If manual image editing is used, then flexibility in editing operations is maintained, but the learning curve and operational difficulty increase
Solution Approach 1:
The system provides a unified object-based editing interface that works across different image types and editing operations. Whether performing color adjustment, cropping, or filtering, users interact with the same object selection mechanism, reducing the need to learn multiple specialized tools while maintaining flexibility in editing operations through consistent, simplified interaction.
Data Source
AI summary
Implementations generally relate to object based image editing. In some implementations, a method includes segmenting an image into object data by identifying one or more object classifications in the image and storing at least one locator for one or more regions of the image corresponding to each instance of the object classification. The method further includes receiving a selection of a representative portion of the segmented image from a user, and matching the representative portion with the object data to determine at least one matched object classification associated with the representative portion. The method further includes presenting the user with one or more of the matched object classifications for the user to instruct one or more edit operations to be applied to at least one object represented by the matched object classification.