Mosaic Image Creation Using Deep Learning Tag Classification
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Solution Overview
Problem
Users face difficulties in selecting source images for photo mosaics that closely resemble a target image, as existing technologies lack efficient methods for categorizing and combining images based on user demands, particularly when specific image categories are required.
Innovation Solution
A method and apparatus using a deep-learning neural network to determine image tag-words, classify images, and provide a user interface for selecting pixel images that match the tag-words or categories, enabling the creation of mosaic images by automatically recommending target and pixel images that satisfy predetermined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually select source images for photo mosaic, then they can choose images they like, but it is difficult to select source images that closely resemble the target image among numerous images
Solution Approach 1:
The system automatically determines tag-words for images using deep learning neural networks and performs classification without user intervention. The server autonomously selects appropriate source images by comparing tag-words and categories, enabling the system to serve itself in the image selection process while maintaining high similarity to the target image.
Solution Approach 2:
The patent replaces manual user selection (mechanical interaction) with automated computer-based image recognition and classification systems. Deep learning neural networks analyze image content and determine tag-words automatically, substituting the manual mechanical process of user selection with intelligent automated processing.
2Quantity of substance
If users need to select numerous source images to create photo mosaic, then they can have enough images to choose from, but it requires significant time and effort to classify and extract appropriate images
Solution Approach 1:
The system performs preliminary classification of images by determining tag-words and categories before the actual mosaic creation process. Images are pre-organized and tagged using deep learning, so when a user wants to create a mosaic, the appropriate source images are already classified and ready for selection, eliminating the need for time-consuming manual classification during the creation process.
Solution Approach 2:
The server automatically performs image classification, tag-word determination, and category assignment without requiring user involvement in these time-consuming tasks. The system serves itself by autonomously processing and organizing the image database, freeing users from the burden of manual image management while maintaining a large pool of categorized source images.
3Productivity
If existing photo mosaic technology is used, then a mosaic image can be created, but there is no effective method to classify and extract source images according to specific user demands or categories
Solution Approach 1:
The system implements a universal image classification framework using deep learning neural networks that can handle multiple types of user demands through tag-words and categories. The same classification system adapts to different scenarios whether users want images by topic, style, or any other categorical criteria, making the system versatile for various mosaic creation needs while maintaining high efficiency.
Data Source
AI summary
Provided is a system and method for creating and providing a mosaic image based on an image tag-word by a mosaic service providing server, which includes: determining each tag-word for each image and classifying a plurality of images according to the determined tag-word; determining a target image among the plurality of images; providing a pixel image selection interface for selecting a pixel image for mosaicizing the determined target image based on the tag-words of the plurality of images; and creating a mosaic image for the target image based on the pixel image selected through the pixel image selection interface.