Pixel-Based Vector Content Prediction for Color Matching
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Solution Overview
Problem
Conventional image-search systems are limited in identifying content that matches specific color preferences or patterns beyond exact keyword matches, making them inefficient and time-consuming, especially in accommodating user preferences that vary over time.
Innovation Solution
A content-prediction application generates vector representations of color palettes from image pixels using convolutional neural networks, allowing for the identification of content items that match color preferences by calculating distance metrics in a color-embedding space, thereby facilitating accurate prediction and recommendation of content items that align with user-defined color schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image-search systems use keyword-based matching, then exact keyword matches can be identified, but the system cannot identify content that matches specific color preferences or patterns
Solution Approach 1:
The patent transforms color palette information into vector representations in a color-embedding space, enabling the system to match content based on color parameters rather than exact keyword matches. This allows flexible adaptation to user color preferences while maintaining search efficiency through mathematical distance calculations.
Solution Approach 2:
The patent introduces a color-embedding space as an intermediary representation between the image database and user color preferences. By converting both images and color palettes into vectors within this embedding space, the system enables efficient similarity search without requiring complex image processing during queries.
2Adaptability or versatility
If the system processes images to extract features for reverse image search, then images with similar content can be identified, but the system cannot accommodate user preferences that vary over time
Solution Approach 1:
The patent pre-processes images by extracting color palettes and converting them into vector representations stored in the database. This preliminary action enables the system to quickly match user color preferences without performing complex image analysis during the search phase, thus accommodating dynamic preferences without time loss.
3Measurement precision
If the system uses exact keyword matching for image search, then precise query results can be obtained, but the retrieval process is inefficient and time-consuming
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching processes with mathematical vector operations in a color-embedding space. By substituting text-based exact matching with geometric distance calculations between color vectors, the system achieves both precise color similarity measurement and efficient retrieval speed.
Data Source
AI summary
Methods and systems disclosed herein relate generally to systems and methods for predicting content based on vector data structures generated from image pixels. A content-prediction application accesses a color palette having two or more color-palette categories and selects a first color of the color palette. The content-prediction application generates a first vector based on a set of pixel values that represent the first color of the color palette. The content-prediction application determines a distance metric between the first vector and a second vector, in which the second vector is identified by applying a convolutional neural network model on an image depicting an item that includes a second color. In response to determining that the distance metric is less than a predetermined threshold, the content-prediction application selects the content item corresponding to the second vector.