Automated Image Selection for Online Product Catalogs
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Solution Overview
Problem
The manual selection of stock images for online product catalogs is inefficient and inconsistent due to human reviewer preferences, leading to time-consuming and varied results.
Innovation Solution
An automated image selection system using a machine learning model trained on labeled images and feature data, including item, seller, and image characteristics, to predict the suitability of images for representing items or categories, thereby selecting the most appropriate stock images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection by human reviewers is used, then personal preferences and subjective judgment are applied, but the process is time-intensive and produces inconsistent results
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated machine learning-based image selection system. The system uses trained models to automatically evaluate and select stock images based on learned patterns from historical data, eliminating the need for time-consuming manual review while ensuring consistent, objective selection criteria are applied to all images.
Solution Approach 2:
The image selection system performs self-service by automatically selecting appropriate stock images without requiring human intervention. The machine learning models independently evaluate images against learned criteria and make selection decisions, enabling the system to serve itself and eliminate the time-intensive manual review process while maintaining reliability through consistent application of selection criteria.
2Productivity
If automated machine learning-based selection is used, then efficiency and consistency are improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical image data and selection criteria before deployment. The models are prepared in advance with learned patterns and knowledge, enabling them to quickly and accurately select images without requiring complex real-time processing. This pre-computation reduces operational complexity while maintaining high productivity during actual image selection tasks.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.