Image Analysis Using Main Color Spatial Distribution Maps
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Solution Overview
Problem
Conventional image analysis methods fail to distinguish between background and objects with similar colors due to the lack of spatial information, leading to difficulties in searching for similar objects in databases.
Innovation Solution
An image analysis apparatus and method that utilize main colors to create binary spatial distribution maps by dividing images into blocks and digitizing them based on the presence of specific main colors, allowing for the representation and comparison of spatial color distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional histogram-based image analysis methods are used, then the analysis process is simple and calculation amount is reduced, but spatial information is lost and background cannot be distinguished from objects with similar colors
Solution Approach 1:
The image is divided into multiple blocks, and each block is analyzed separately to determine its main color. This segmentation preserves spatial information by maintaining the positional relationship of different regions while simplifying the analysis of each individual block.
Solution Approach 2:
Different blocks in the image are analyzed with respect to their local color characteristics rather than treating the entire image uniformly. This allows each region to be evaluated based on its own main color, preserving local spatial information while keeping the overall process simple.
2Device complexity
If histogram-based color analysis is used, then calculation complexity is reduced, but the ability to represent spatial color distribution is lost
Solution Approach 1:
The image is segmented into blocks, and a binary map is created for each block indicating the presence of its main color. This segmentation approach preserves spatial color distribution information by maintaining the location-specific color characteristics while keeping calculations simple.
Solution Approach 2:
The analysis transitions from a global histogram approach to a block-based dimensional approach, where each block contributes its own color information to the overall spatial distribution. This dimensional change preserves spatial information while maintaining computational simplicity.
3Speed
If conventional image analysis methods are used, then processing speed is maintained, but similarity search accuracy for objects with similar colors deteriorates
Solution Approach 1:
By dividing the image into blocks and analyzing each block's main color separately, the method maintains processing speed through efficient block-level operations while improving similarity search accuracy by preserving the spatial arrangement of different color regions.
Solution Approach 2:
Each block is analyzed with respect to its local main color characteristics, enabling more accurate identification of objects with similar colors by considering their spatial distribution patterns rather than treating all pixels uniformly.
Data Source
AI summary
A method of controlling an image analysis apparatus is provided. The method includes receiving a query image; determining at least one main color distributed in the query image and a specific main color from among the at least one main color by using color information contained in the query image; dividing the query image into at least one block having a predetermined number of pixels; and determining whether each of the at least one block includes more than a predetermined percentage of a main color for comparison among the at least main color, and creating a binary spatial distribution map by digitizing the at least one block.