Image Keypoint Matching for Cross-Listed Property Detection
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Solution Overview
Problem
Existing systems fail to accurately identify whether a product is cross-listed across different platforms, especially when listings use different images, text, addresses, and descriptive information, making it difficult for merchants to determine if a unique or scarce product is being offered on multiple platforms.
Innovation Solution
The implementation of a system that compares image keypoints and descriptors between product listings from different platforms using algorithms like A-KAZE, combined with machine learning for categorization, to determine if listings represent the same product, even with varying image angles and qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to identify cross-listed products, then accuracy can be maintained, but productivity and time consumption are severely limited
Solution Approach 1:
The patent replaces manual review (mechanical human inspection) with an automated computer vision system using image processing algorithms. The system extracts features from product images, compares them across listings, and automatically identifies cross-listed products, thereby substituting human labor with computational processes that achieve both high accuracy and high productivity
Solution Approach 2:
The system enables self-service by allowing the platform to automatically detect and identify cross-listed products without requiring merchant intervention. The automated image comparison system performs the identification task independently, freeing merchants from manual review while maintaining high accuracy through algorithmic analysis
2Productivity
If automated image comparison systems are implemented, then productivity increases, but measurement precision decreases due to variations in image quality, angles, and descriptions
Solution Approach 1:
The patent segments the image comparison task into multiple independent feature extraction steps: color analysis, texture analysis, shape analysis, and key feature point detection. By dividing the complex matching problem into separate analytical components, the system can evaluate each aspect independently and combine results, maintaining high precision even with varying image qualities and angles
Solution Approach 2:
The system transforms the image comparison problem from direct pixel-by-pixel comparison into a multi-parameter feature space analysis. It extracts and compares parameters such as dominant colors, texture patterns, geometric shapes, and key feature points, which are more robust to variations in image quality, lighting, and viewing angles than raw pixel data
3Measurement precision
If comprehensive product information is collected across platforms, then measurement precision improves, but device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the most discriminative and relevant features from product listings: image features (colors, textures, shapes, key points) and essential text information. By selecting and extracting only the critical features needed for identification rather than processing all available data, the system achieves high precision while keeping computational complexity manageable
Data Source
AI summary
Two sets of data, each containing property listings, are obtained from two discrete merchant platforms. Each property listing in a set of data of a first merchant is sequentially paired with each of the property listings in a set of data of a second merchant. For each pair, each image of the property listing of the first merchant is compared to each image of the property listing of the second merchant, and images of statistically sufficient similarity are identified. The similarity of images, and in particular, of similar images likely to be rooms of the property, are considered in a determination of whether the product listings of the first and second merchant are for the same cross-listed product.