Part-Wise Color Extraction for Product Representation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing technologies struggle to accurately extract and represent the nuanced color compositions of individual product parts, leading to imprecise search results and user dissatisfaction in e-commerce and product recommendation systems.
Innovation Solution
A deep learning-based system that identifies and segregates product pixels, assigns them to specific parts, and extracts detailed color information using a predefined color palette, creating a two-dimensional color representation matrix for precise color analysis and matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color histograms or global color clustering are used, then the processing is simple and fast, but the color representation is imprecise and does not capture part-specific color information
Solution Approach 1:
The patent applies segmentation by dividing the product image into multiple distinct parts (e.g., shoe upper, sole, laces) and extracting color information for each part separately. This is achieved through part identification modules that segment the image based on product structure, enabling precise color representation for each component rather than treating the entire image as a single unit.
Solution Approach 2:
The patent introduces an intermediary color palette as a mediator between the extracted pixel colors and the final color representation. The system compares extracted colors against a predefined palette to determine the closest matching colors, thereby transforming raw pixel data into standardized, interpretable color information that enhances precision while maintaining system manageability.
2Measurement precision
If part-wise color extraction is implemented, then the color analysis accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-defining a color palette and preparing part segmentation schemas before actual color extraction. The system pre-identifies product parts and their boundaries, so that during color extraction, the system only needs to compare pixel colors against the existing palette and assigned parts, significantly reducing real-time computational burden while maintaining high accuracy.
3Reliability
If deep learning models are used for product and part identification, then the accuracy of pixel segmentation is improved, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent applies copying by using deep learning models to create accurate copies or masks of product parts from the original image. The model generates segmentation masks that replicate the exact boundaries and regions of different product components, which can then be used for color extraction without requiring the full complexity of the deep learning model during the actual color analysis phase, thus reducing ongoing computational requirements.
4Productivity
If detailed color profiles for each part are created, then the product search relevance is improved, but the data processing and storage requirements increase
Solution Approach 1:
The patent applies local quality by creating detailed color profiles specifically for each product part rather than generating a single overall color profile. Each part receives customized color analysis based on its specific characteristics and position, allowing the system to store and process color data in a targeted manner that maximizes search relevance while minimizing redundant data generation.
Data Source
AI summary
The present invention discloses a system for multi-color product representation through part-wise color extraction and corresponding method. The system (100) includes an image retrieval module (101) for fetching product images, a product identification module (102) using deep learning model engines to isolate product pixels, a part identification module (103) for distinguishing product parts, a color extraction module (104) for computing color percentages with a set color palette, and a color representation module (105) for creating a two-dimensional color vector. Finally, a color matching module (106) ensures precise product color comparisons using the color vector distance measurement between products.