Image Processing Device for Automatic Object Association
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Solution Overview
Problem
The existing image tagging techniques require users to manually specify image recognition engines and model dictionaries, which becomes burdensome as the number of images increases, making it difficult to efficiently associate images with objects.
Innovation Solution
An image processing device that includes an attribute storage unit, object information storage unit, extracting unit, specifying unit, and associating unit to automatically identify and associate images with objects based on common photographic attributes, eliminating the need for user specification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual specification of image recognition engines and model dictionaries is required, then accurate object tagging can be achieved, but user burden increases significantly as image collection size grows
Solution Approach 1:
The system automatically extracts common photographic attributes from image metadata and performs autonomous object classification without requiring user intervention. The extracting unit retrieves photography-related information from multiple images, identifies common attributes, and the specifying unit automatically selects appropriate model dictionaries and recognition engines based on these attributes, enabling the system to serve itself rather than requiring manual user configuration.
Solution Approach 2:
The system pre-extracts and stores photographic attributes from image metadata before the actual object recognition process. By preparing the attribute information in advance and organizing it into structured data, the system eliminates the need for users to manually specify recognition parameters when processing large image collections, significantly reducing operational burden while maintaining tagging accuracy.
2Adaptability or versatility
If multiple image recognition engines and model dictionaries are prepared for different themes, then comprehensive object recognition is enabled, but system complexity increases
Solution Approach 1:
The extracting unit serves multiple functions by simultaneously retrieving photography-related information from image files, extracting common photographic attributes, and organizing this information for use by the specifying unit. This multi-functional approach consolidates what would otherwise require separate dedicated components, reducing system complexity while maintaining comprehensive object recognition capabilities across different themes and image types.
Solution Approach 2:
The common photographic attributes extracted from image metadata serve as an intermediary layer between the raw image data and the object recognition process. These attributes act as a bridge that automatically connects image characteristics to appropriate model dictionaries and recognition engines, eliminating the need for complex manual configuration and simplifying the overall system architecture while preserving versatility.
3Ease of operation
If automatic extraction of common photographic attributes is performed, then user burden is reduced, but processing time increases
Solution Approach 1:
The system extracts and stores common photographic attributes from image metadata in advance, before the actual object recognition and tagging process. By preparing this attribute information beforehand and organizing it into structured formats, the system eliminates the need for real-time user intervention during processing, significantly reducing operational burden while the pre-computed attributes accelerate subsequent recognition operations.
Data Source
AI summary
Provided is an image processing device for associating images with objects appearing in the images, while reducing burden on the user. The image processing device: stores, for each of events, a photographic attribute indicating a photographic condition predicted to be met with respect to an image photographed in the event; stores an object predicted to appear in an image photographed in the event; extracts from a collection of photographed images a photographic attribute that is common among a predetermined number of photographed images in the collection, based on pieces of photography-related information of the respective photographed images; specifies an object stored for an event corresponding to the extracted photographic attribute; and conducts a process on the collection of photographed images to associate each photographed image containing the specified object with the object.