Image Categorization via User Comparison and Classification
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Solution Overview
Problem
Current methods for categorizing images in online auctions lack efficiency in accurately describing items based on visual representations, making it difficult for buyers to search and assign appropriate categories.
Innovation Solution
A system that displays two or more images to users, allowing them to compare and input differences or similarities, which are then classified into categorization data to assign images into categories, utilizing a module that includes classification and assignment functions within a networked system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image categorization methods are used, then processing speed is improved, but categorization accuracy deteriorates
Solution Approach 1:
The patent introduces image comparison as an intermediary process between automated image analysis and final categorization. By comparing images against reference images and identifying similarities/differences, the system bridges the gap between automated processing and accurate categorization, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The categorization process is segmented into multiple stages: initial automated image analysis, image comparison against references, difference/similarity identification, and final categorization assignment. This segmentation allows each stage to be optimized independently, maintaining both processing efficiency and categorization accuracy.
2Measurement precision
If manual image categorization is used, then categorization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automated image analysis and comparison against reference images before final categorization. This preliminary action pre-processes the images and identifies key features, reducing the time required for manual or final categorization while maintaining accuracy.
Solution Approach 2:
The system enables self-service categorization by automatically comparing images against reference images and generating categorization recommendations. This reduces reliance on manual categorization while maintaining accuracy, allowing the system to serve itself in the categorization process.
3Measurement precision
If detailed image analysis is performed, then categorization accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential features and differences needed for categorization from detailed image analysis. By taking out only the relevant comparison elements rather than analyzing all image details, the system maintains categorization accuracy while reducing computational complexity.
Solution Approach 2:
The system applies different levels of analysis quality to different parts of the image based on their relevance to categorization. Rather than uniformly analyzing all image regions in detail, it focuses computational resources on locally important features, maintaining accuracy while reducing overall system complexity.
Data Source
AI summary
In an example embodiment, a method is provided for image categorization. Here, images are displayed. In turn, a user input that describes a characteristic shared between the images from a comparison between the images is received. The user input may then be classified into categorization data.