Image Sorting via Feature Identification Area Extraction
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Solution Overview
Problem
Existing image sorting methods in electronic devices rely on generation time, which is inefficient for users needing to quickly and conveniently search and process images based on content, especially when dealing with large quantities of similar images.
Innovation Solution
An image sorting method that involves determining a feature identification area in each image, recognizing the content within these areas, and sorting images based on that content, allowing for flexible and content-based sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are sorted by generation time, then the sorting process is simple and fast, but the user cannot quickly search and process images based on content
Solution Approach 1:
The patent divides the image into a feature identification area and other areas. The feature identification area contains content used for sorting (such as document numbers, titles, or other distinctive text), while the rest of the image is preserved for display. This segmentation allows the system to extract sorting features efficiently without processing the entire image, thus improving sorting efficiency while enabling content-based search.
Solution Approach 2:
The patent extracts the feature identification area from each image, which contains the key content information needed for sorting. By taking out only the essential feature content (such as text in specific regions) rather than analyzing the whole image, the system achieves fast content-based sorting that improves both sorting efficiency and search convenience.
2Ease of operation
If images are sorted by content, then the user can quickly search and process images, but the sorting process becomes more complex
Solution Approach 1:
The patent simplifies content-based sorting by segmenting only the feature identification area for analysis rather than processing the entire image. This reduces the complexity of the sorting process while still enabling content-based search, as the system only needs to analyze the extracted feature area for sorting purposes.
Solution Approach 2:
The patent extracts only the necessary feature content from the feature identification area for sorting purposes, rather than analyzing the complete image. This extraction approach reduces computational complexity while maintaining the ability to sort and search by content, thus lowering device complexity requirements.
3Loss of information
If the entire image is analyzed for sorting, then comprehensive content information is obtained, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the image to identify and extract only the feature identification area that contains sorting-relevant content. This segmentation approach maintains completeness of necessary content information while reducing processing time, as the system only analyzes the extracted feature area rather than the entire image.
Solution Approach 2:
The patent extracts the feature identification area containing essential content information needed for sorting. By taking out only this relevant portion for analysis, the system obtains sufficient content information for accurate sorting while significantly reducing processing time and computational resource consumption.
Data Source
AI summary
An image sorting method includes obtaining a plurality of images that need to be sorted, determining a feature identification area of an image, recognizing the content of the feature identification area of each of the images in sequence to obtain a feature content of the feature identification area of each of the images, and sorting the plurality of images based on the feature content of the feature identification area of each of the images. Content of the feature identification area is used to distinguish different images.