AI Object Segmentation for Distractor Removal in Digital Images
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits at the pixel level and failing to anticipate and prepare for modifications to objects within digital images.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, generating object masks and content fills, and creating semantic scene graphs to facilitate intuitive and efficient object-aware modifications, allowing users to interact with digital images as if they were real scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems perform pixel-level edits, then editing precision is maintained, but user interaction complexity and time consumption increase significantly
Solution Approach 1:
The system segments the image into multiple objects using object detection and segmentation models, allowing users to interact with entire objects rather than individual pixels. This segmentation enables selective editing of specific objects while automatically handling the rest of the image, dramatically reducing user interaction complexity and editing time.
Solution Approach 2:
The system performs preliminary actions by automatically detecting objects, generating masks, and preparing edit candidates before user interaction. The system pre-processes the image to identify editable regions and potential edit outcomes, so when users make selections, the actual editing execution is rapid and requires minimal additional time.
2Productivity
If machine learning models are used to pre-process images and generate object masks, then editing efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including object detection models, segmentation models, and edit generation models that act as mediators between user input and final image editing. These intermediaries automatically handle the complex task of understanding image content and generating appropriate edits, improving productivity while managing system complexity through modular architecture.
Solution Approach 2:
The system enables self-service by allowing users to simply select objects or regions of interest without needing to understand complex editing parameters or techniques. The machine learning models automatically analyze the selection, generate appropriate edit candidates, and present options to the user, making the system easy to use despite the underlying complexity.
3Adaptability or versatility
If the system generates multiple edit candidates with different outcomes, then editing flexibility is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by generating a limited number of curated edit candidates rather than exhaustively exploring all possible edits. The edit generation model focuses on producing a small set of high-quality, diverse edit options that are most likely to be useful, balancing flexibility with processing efficiency by avoiding excessive computation.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems provide, for display within a graphical user interface of a client device, a digital image displaying a plurality of objects, the plurality of objects comprising a plurality of different types of objects. The disclosed systems generate, utilizing a segmentation neural network and without user input, an object mask for objects of the plurality of objects. The disclosed systems determine, utilizing a distractor detection neural network, a classification for the objects of the plurality of objects. The disclosed systems remove at least one object from the digital image, based on classifying the at least one object as a distracting object, by deleting the object mask for the at least one object.