Image Sharing System with Automatic Subject Classification
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Solution Overview
Problem
Conventional image sharing systems fail to effectively classify and share images that do not include people, such as plants or scenery, and often overwhelm users with large volumes of unorganized data, making it difficult for recipients to browse and manage shared images.
Innovation Solution
An image sharing system that includes an image acquiring means, a subject assessing means, an image associating means, and a shared image determining means to classify and share images based on user preferences, allowing non-person images to be associated with person images and organized into user-specific groups, with sharing rules and layout information used to create easily accessible and organized shared pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all image data are classified by face detection, then images including people can be classified, but images without people (plants, scenery) cannot be classified and shared
Solution Approach 1:
The classification process is divided into two independent stages: first, face detection and recognition for images containing people; second, alternative classification methods (such as scene recognition, object detection, or keyword association) for images without people. This segmentation allows the system to handle both types of images with appropriate methods without forcing a single classification approach on all images.
Solution Approach 2:
The system changes the classification parameters based on image content characteristics. For images with faces, facial recognition parameters are used; for images without faces, alternative parameters such as scene categories, detected objects, or metadata keywords are employed. This parameter adaptation enables versatile classification across different image types while maintaining high accuracy for each category.
2Quantity of substance
If large volume of images are shared among users, then more images can be shared, but users find it inconvenient to browse and organize the images
Solution Approach 1:
The large volume of shared images is segmented and organized into multiple categories based on face detection results, scene types, objects, or keywords. Each category acts as an independent browsing unit, allowing users to navigate through images systematically rather than facing a disorganized mass of files. This segmentation dramatically improves browsing convenience while maintaining the ability to share large quantities of images.
Solution Approach 2:
An intelligent intermediary system (the image sharing server with classification capabilities) is introduced between the image source and the user. This intermediary automatically performs classification, tagging, and organization of images before they reach the user, eliminating the need for users to manually sort through large volumes of images. The intermediary handles the organizational complexity while presenting users with a well-structured, easy-to-browse collection.
3Ease of manufacture
If images are classified based on date, event, or location, then images can be organized, but images cannot be classified according to user preferences or personal interest
Solution Approach 1:
The image classification system is designed with multi-functionality, supporting multiple classification dimensions simultaneously: temporal (date), contextual (event, location), visual (face recognition, scene type), and personalized (user preferences). This universal approach allows the same system to serve different organizational needs and user preferences without requiring separate systems for each classification method.
Solution Approach 2:
The classification system dynamically adapts to user preferences and can adjust classification priorities based on individual user profiles. Users can specify their interests, and the system dynamically reweights or reorders classification results accordingly. This dynamic capability allows the system to maintain structured organization while simultaneously adapting to personal preferences, resolving the contradiction between fixed organization methods and flexible personalization.
Data Source
AI summary
The saving device for image sharing includes an image acquiring unit configured to acquire the images offered by a sharer of the images, a sharee information storing unit configured to store sharee information with respect to at least one sharee, a subject assessing unit configured to assess whether or not a person subject is included in the acquired images, an image associating unit configured to associate the images assessed as not including a person subject with the images assessed as including a person subject, based on the sharee information, and a shared image determining unit configured to determine the images to be shared with the sharee or sharees from among the associated images and the images assessed as including a person subject, based on the sharee information. The image sharing system and an image sharing method use such a device.