Intent-Driven Object Editing for Efficient Image Deletion
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits on a pixel level, lacking the ability to intuitively edit digital images as real scenes and maintain real-world conditions.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, identifying objects and their relationships, generating object masks and content fills, and enabling intuitive, object-level editing by anticipating user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image editing systems perform pixel-level editing, then editing precision is improved, but user interaction complexity increases significantly
Solution Approach 1:
The patent segments the image into meaningful objects using object detection and segmentation models. Instead of requiring users to edit individual pixels, the system identifies and segments distinct objects (e.g., persons, vehicles, buildings) so users can interact with entire objects as cohesive units, dramatically reducing interaction complexity while maintaining editing precision at the object level
Solution Approach 2:
The patent introduces an intermediary layer of object masks and semantic understanding between the user and the pixel data. The system generates object masks that represent meaningful regions, and users interact with these masks rather than raw pixels. This intermediary abstraction layer simplifies user interaction while preserving the ability to make precise edits to specific image regions
2Productivity
If machine learning models pre-process images to identify objects and relationships, then editing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing images with machine learning models to identify objects, generate masks, and understand relationships before the user performs any editing. The system anticipates potential edits by pre-segmenting objects and preparing object-level representations, so when users interact with the image, the heavy computational work has already been completed, improving editing efficiency while managing complexity through staged processing
Solution Approach 2:
The system performs self-service by automatically generating object masks, identifying relationships between objects, and preparing edit suggestions without requiring user input. The machine learning models autonomously analyze the image structure and relationships, reducing the need for complex user-system interactions and improving efficiency by having the system serve itself in the pre-processing stage
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images using an intelligent user interface tool that determines the intent of a user interaction. For instance, in some embodiments, the disclosed systems receive, via a graphical user interface of a client device, a user interaction with a set of pixels within a digital image. The disclosed systems determine, based on the user interaction, a user intent for targeting one or more portions of the digital image for deletion, the one or more portions including an additional set of pixels that differs from the set of pixels. Based on the user intent, the disclosed systems modify the digital image to delete the one or more portions from the digital image