Image Recommendation System Using Feature Amount Classification
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Solution Overview
Problem
Existing systems fail to recommend images that do not belong to specific categories, such as travel or subject-based classifications, limiting user engagement and utilization of diverse image collections.
Innovation Solution
An information presenting apparatus that determines user interest categories based on image features, parameters, and metadata, extracting and presenting images and accessory information from similar categories, promoting varied image usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recommendation is based on specific categories (travel, subject-based), then recommendation accuracy for those categories is improved, but the system cannot recommend images that do not belong to specific categories
Solution Approach 1:
The patent changes the parameter for image classification from traditional category-based approaches to feature amount-based approaches. By analyzing feature amounts (such as color histograms, texture features, and other image characteristics) rather than relying on predefined categories, the system can accurately recommend images across all types including those that don't fit traditional categories, thus improving both recommendation accuracy and coverage simultaneously
Solution Approach 2:
The patent creates a universal recommendation system that can handle all image types through a single feature amount analysis mechanism. Instead of requiring separate recommendation mechanisms for different categories, the system uses a unified approach that analyzes image features to generate recommendations applicable to any image type, making the system multi-functional and broadly adaptable
2Ease of manufacture
If traditional category-based classification is used, then images can be organized into standard categories, but images without clear categories (such as failed images, screen captures) cannot be recommended
Solution Approach 1:
The patent transitions from category-based classification to feature amount-based classification. By analyzing the actual visual features and characteristics of images rather than forcing them into predefined categories, the system can successfully classify and recommend images of any type, including failed images and screen captures that lack traditional category assignments, thereby improving image usability while maintaining classification organization
Data Source
AI summary
An object of the present invention is to provide an information presenting apparatus, method, and a program capable of promoting a user to variously use images according to a category to which an image owned by a user belongs.The category classifying unit classifies images uploaded from the first user terminal and the second user terminal to the SNS server by machine learning into any one of defined categories such as “nature field”, “art field”, “wedding field”, “snap field” and the like. The category determining unit determines the category of the trigger image identified by the trigger image identification information. The recommended information extracting unit extracts images (recommended images) belonging to the same category as the category determined by the category determining unit. The recommended information presenting unit presents the recommended image and/or recommended image related information extracted by the recommended information extracting unit, to the first user terminal as recommended information.