Product Image Evaluation System for Deduplication and Curation
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Solution Overview
Problem
Current product image selection systems fail to provide an optimal set of images that are sufficiently different, relevant, and of high quality, often including duplicates or irrelevant images, which can hinder customer purchasing decisions due to subjective human curation and lack of consideration for multiple product views.
Innovation Solution
A data-driven approach involving a product image evaluation system that uses pairwise identification of near-duplicate images, clustering, and selection based on parameters like resolution, sharpness, and relevance, utilizing algorithms such as perception hash and cosine similarity, to curate a meaningful and ordered set of images from disparate sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human reviewers manually curate image sets, then subjective selection can be avoided, but the process is too slow and biased for large numbers of products
Solution Approach 1:
The patent replaces the mechanical system of human reviewers with an automated image evaluation system that uses computer algorithms to objectively assess and select product images. The system employs multiple evaluation criteria including image quality metrics, deduplication algorithms, and relevance scoring to automatically curate image sets without human intervention, thereby eliminating subjectivity while maintaining high productivity across large product catalogs.
2Quantity of substance
If deduplication systems eliminate similar images, then duplicate images are removed, but useful product views with similar image data but different information are lost
Solution Approach 1:
The patent applies local quality by evaluating different regions and aspects of images independently. Instead of treating entire images as identical based on overall similarity, the system analyzes specific local features such as product angles, lighting conditions, background elements, and compositional details. This allows the system to distinguish between truly duplicate images and images that show different views or perspectives of the same product, preserving valuable product information while removing actual duplicates.
3Productivity
If automated systems select images based on data alone, then processing speed increases, but subjective human judgment and contextual understanding are lost
Solution Approach 1:
The patent segments the image selection process into multiple independent evaluation components, each handling a specific aspect of image quality and relevance. The system divides the overall selection task into sub-tasks such as image quality assessment, deduplication analysis, relevance scoring, and ranking, with each segment processed by specialized algorithms. This segmentation allows the automated system to achieve both high processing speed and reliable selection quality by applying appropriate evaluation methods to each aspect independently and combining the results.
Data Source
AI summary
A product image evaluation system. In embodiments, the system comprises or interacts with a product database comprising a product information record that comprises a product identifier and a product category for a product, and an image database comprising a plurality of candidate images for the product. In embodiments the image database can comprise images received from a plurality of different sources. The system can comprise a parameterized grouping engine configured to separate images into groups of similar images, an image selector configured to select one or more images from each group, and an image sorter configured to determine an order of the selected images. Embodiments can distill the superset of all available images to provide a set of images that are “sufficiently different” from each other and satisfy quality requirements. As a result, no images containing unique information are left behind, and images containing duplicate or irrelevant information are discarded.