Automated Image Masking for E-Commerce Product Listings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online shopping platforms face inefficiencies due to the manual removal of extraneous visible elements from images, leading to delayed product listings, increased labor costs, and a negative impact on sales, as human operators must manually edit images to comply with submission guidelines.
Innovation Solution
An automated image processing system that uses multiple modules and machine learning techniques to remove extraneous elements such as text, logos, and watermarks from input images, producing candidate images that comply with submission guidelines, thereby reducing human intervention and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image editing is used to remove extraneous elements, then image quality and compliance with submission guidelines are maintained, but processing time and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical image editing with an automated computer-based image processing system that uses machine learning models and algorithms to detect and remove extraneous elements, watermarks, and text from images automatically, eliminating the need for human operators while maintaining compliance quality
Solution Approach 2:
The system enables images to process themselves by automatically detecting extraneous elements and performing removal operations without human intervention, with the image processing system serving its own compliance needs through automated quality checks and iterative refinement
2Reliability
If multiple image processing operations are performed to remove all extraneous elements, then submission guideline compliance is improved, but system complexity and processing time increase
Solution Approach 1:
The patent divides the image processing system into multiple specialized modules, each responsible for detecting and removing specific types of extraneous elements (watermarks, text, logos, etc.), allowing the complex task to be broken down into manageable, independent processing stages that can be executed sequentially
Solution Approach 2:
The system employs a universal image processing framework that handles multiple types of extraneous elements through a common architecture, where a single processing pipeline can detect and remove various element types using different detection algorithms and removal techniques within the same system
3Productivity
If automated image processing is implemented, then processing speed and productivity are improved, but the ability to handle complex extraneous elements may be compromised
Solution Approach 1:
The system performs preliminary detection and classification of extraneous elements before executing removal operations, using machine learning models to pre-identify watermarks, text, and other elements, allowing the processing system to prepare appropriate removal strategies in advance and execute them efficiently
Solution Approach 2:
The image processing system incorporates feedback mechanisms where processing results are evaluated and used to refine subsequent processing steps, with automated quality checks detecting remaining extraneous elements and triggering iterative removal passes until compliance standards are met
Data Source
AI summary
An input image of an object is prepared for presentation by removing extraneous portions such as text, logos, advertising, watermarks, and so forth. The input image is processed to determined contours of features depicted in the input image. A bounding box corresponding to each contour may be determined. Based at least in part on the areas of these bounding boxes, an image mask is created. A candidate image is determined by applying the image mask to the input image to set pixels within portions of the input image to a predetermined value, such as white. Many candidate images may be generated using different parameters, such as different thresholds for relative sizes of the areas of the bounding boxes. These candidate images may be assessed, and a candidate image is selected for later use. Instead of manual editing of the input images, the candidate images are automatically generated.