Object-Agnostic Image Encoding for Background-Based Retrieval
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Solution Overview
Problem
Conventional image representation systems focus on encoding salient objects within images, failing to accurately represent background content, which is necessary for applications like image compositing where users want to match backgrounds without considering foreground objects.
Innovation Solution
An image processing apparatus that removes foreground elements, inpaints the space they occupy, and trains a machine learning model to encode both the original and inpainted images into similar representations, using contrastive learning to distinguish between positive and negative sample pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image representation systems focus on encoding salient objects, then object recognition accuracy is improved, but background representation capability deteriorates
Solution Approach 1:
The patent segments the image into foreground objects and background components by removing foreground elements and creating inpainted versions. This segmentation allows the model to separately encode background information while maintaining object awareness, resolving the contradiction between focusing on objects and preserving background representation.
Solution Approach 2:
The inpainted image serves as an intermediary that fills the gap between the original image and the foreground-removed image. By training on both the original and inpainted images, the model learns to represent backgrounds while still accounting for object presence, thus maintaining both object recognition accuracy and background representation capability.
2Productivity
If conventional image representations are used, then object-based search is effective, but background-based search capability is lost
Solution Approach 1:
The patent creates a universal image representation model that can serve multiple functions: object-based search, background-based search, and image compositing. By training on paired original and inpainted images, the model learns to encode both object and background information, making it adaptable to different search queries and applications.
Solution Approach 2:
The model dynamically adjusts its encoding parameters based on the query type. When searching for objects, it emphasizes object features; when searching for backgrounds, it emphasizes background features. This parameter adaptation allows the same model to effectively handle both object-based and background-based search requirements.
3Loss of information
If foreground objects are removed from images, then background representation is improved, but image completeness deteriorates
Solution Approach 1:
The model performs preliminary action by training on inpainted images that fill in the removed foreground areas. This preliminary training enables the model to reconstruct complete image representations while maintaining focus on background information, thus improving background representation without sacrificing image completeness.
Solution Approach 2:
The training process uses feedback from comparing original and inpainted images to continuously improve the model's ability to represent backgrounds. The model learns from the differences between these images, adjusting its encoding parameters to better capture background characteristics while maintaining overall image completeness.
Data Source
AI summary
Systems and methods for image processing, and specifically for generating object-agnostic image representations, are described. Embodiments of the present disclosure receive a training image including a foreground object and a background, remove the foreground object from the training image to obtain a modified training image, inpaint a portion of the modified training image corresponding to the foreground object to obtain an inpainted training image, encode the training image and the inpainted training image using a machine learning model to obtain an encoded training image and an encoded inpainted training image, and update parameters of the machine learning model based on the encoded training image and the encoded inpainted training image.