Image Classification via Color Order Reduction and Block Analysis
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Solution Overview
Problem
The increasing number of images shared on social networks leads to a lack of effective classification and display methods, making it difficult for users to browse and view images conveniently.
Innovation Solution
An image classifying method that reduces the color order of images to generate blocks, calculates block color difference values, and classifies images into categories, accompanied by an image displaying method that adjusts display parameters based on image scores to prioritize higher-scoring images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are classified using detailed color analysis, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the image into multiple blocks and performs color order reduction on each block separately. This segmentation allows the system to analyze color characteristics at a coarser level (block level rather than pixel level), reducing computational complexity while maintaining sufficient classification accuracy through aggregated block color difference values.
Solution Approach 2:
The patent transforms the original color space parameters into a reduced color order representation. By changing the parameter representation from full-color pixel data to reduced color order blocks, the system achieves lower computational complexity while preserving the essential color information needed for accurate image classification.
2Quantity of substance
If all images are displayed without prioritization, then display completeness is improved, but user browsing efficiency deteriorates
Solution Approach 1:
The patent performs preliminary classification and scoring of images before display. By pre-categorizing images into themes and pre-calculating popularity scores, the system prepares the data in advance, allowing users to efficiently browse popular or relevant images first while maintaining access to the complete image set through the classification structure.
Solution Approach 2:
The patent introduces an additional organizational dimension by classifying images into multiple themes/categories alongside the display prioritization. This multi-dimensional organization allows users to navigate both by popularity (efficiency) and by category (completeness), resolving the contradiction between browsing efficiency and display completeness.
3Ease of operation
If images are categorized into multiple themes, then user convenience is improved, but classification complexity increases
Solution Approach 1:
The patent segments the classification task into manageable components: color order reduction, block-based color difference calculation, and theme assignment. This segmentation makes the classification process more manageable and less complex while still achieving multi-theme categorization that improves user convenience through organized browsing.
Data Source
AI summary
An image classifying method includes the following operations: reducing an order of a color of a first image to generate a first order reduction image, wherein the first order reduction image includes several first image blocks; obtaining a second order reduction image from a database, wherein the second order reduction image includes several second image blocks; calculating several block color difference values between the first order reduction image and the second order reduction image respectively according to differences between a color value of each of the first image blocks and a color value of each of the second image blocks; and determining whether or not the first image belongs to a same category as the second order reduction image according to the block color difference values between the first order reduction image and the second order reduction image.