Histogram-Based Image Matching Using Subset Bin Selection
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Solution Overview
Problem
Existing image retrieval systems face inefficiencies in comparing digital images due to computationally expensive pixel-by-pixel comparisons, necessitating improved methods for fast and accurate image matching using histograms.
Innovation Solution
The system generates and compares normalized histograms by selecting a subset of bins representing the largest values, calculating a similarity score based on overlap and total values, and using these measures to identify similar images within an image retrieval system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel comparison is used for image matching, then measurement precision is improved, but productivity deteriorates due to computational expense
Solution Approach 1:
The image is segmented into discrete color bins in the histogram, transforming continuous pixel data into discrete frequency distributions. This segmentation enables efficient comparison by operating on bin counts rather than individual pixels, resolving the contradiction between precision and speed.
Solution Approach 2:
The histogram serves as an intermediary representation between the original image and the comparison process. Instead of directly comparing pixels, the system compares histogram distributions, which act as a compressed intermediary that preserves essential image characteristics while enabling faster processing.
2Measurement precision
If complete histogram comparison is performed, then measurement precision is improved, but loss of time increases due to processing all bins
Solution Approach 1:
The method extracts only the most significant bins from the histogram based on their contribution to the overall distribution. By taking out and processing only these key bins rather than all bins, the system reduces comparison time while maintaining sufficient accuracy for effective image retrieval.
Solution Approach 2:
The system performs partial comparison by focusing on the most informative bins rather than exhaustively comparing all bins. This partial action approach achieves sufficient similarity measurement accuracy without the time cost of complete histogram comparison.
3Reliability
If normalization is applied to histograms, then reliability of comparison is improved, but device complexity increases
Solution Approach 1:
The histogram data undergoes parameter transformation through normalization, converting raw bin counts into a standardized scale. This parameter change ensures that comparisons are reliable and consistent across images with different characteristics, while the normalization process itself adds manageable complexity.
Data Source
AI summary
A system and method for comparing and searching for digital data, such as images, using histograms includes receiving a source image, receiving a comparison image, generating a first histogram for the source image and generating a second histogram for the comparison image. The source image may be received from a network device, such as a computer or camera, and the comparison image may one of a plurality of stored images in a database. The histograms may correspond to an image characteristic, including a color histogram corresponding to the distribution of the intensity of a corresponding color among image pixels in the source image. Each of first and second histograms is normalized and a similarity score is calculated between the two histograms. The similarity score represents a similarity measure between the two histograms, calculated from a subset of bins, which are independently selected for each image.