Graphic Equalizer Weighting for Image Search Efficiency
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Solution Overview
Problem
Current electronic devices face challenges in efficiently analyzing information due to increased demands for processing power and hardware resources, leading to economic impacts and operational inefficiencies, particularly in handling complex digital image data.
Innovation Solution
A system and method utilizing a graphic equalizer to perform image search procedures by creating color histograms and applying weighting parameters to calculate weighted distance measures, combining them to identify similar images through composite distance measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enhanced device capability to perform advanced image analysis operations is implemented, then image search efficiency and analysis quality are improved, but processing power requirements and hardware resources increase
Solution Approach 1:
The patent segments the image analysis task by creating multiple color histograms at different levels of detail (fine-detail, medium-fine detail, medium-coarse detail, and coarse-detail). Each histogram level processes specific aspects of image data independently, allowing the system to analyze images efficiently without requiring excessive processing power for the entire complex analysis at once.
Solution Approach 2:
The patent applies different levels of detail to different aspects of image analysis. By using a graphic equalizer to assign weighting parameters to each histogram level, the system prioritizes computation resources toward the most important detail levels for a given search task, improving efficiency while maintaining quality where needed.
2Measurement precision
If enhanced device capability for image manipulation is implemented, then image search accuracy is improved, but hardware resources and production costs increase
Solution Approach 1:
The patent divides the image manipulation and search accuracy task into multiple histogram calculations at different detail levels. This segmentation allows the system to achieve high accuracy through combined results from multiple simpler operations rather than requiring a single complex high-cost operation.
Solution Approach 2:
The patent creates a multi-functional system where a single graphic equalizer-based framework can handle various image search tasks by adjusting weighting parameters. This universal approach improves accuracy for different search criteria without requiring separate specialized hardware for each function, thereby reducing production costs.
3Measurement precision
If detailed color histogram analysis is performed, then image search precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the computational task into four distinct histogram calculation levels (fine-detail, medium-fine detail, medium-coarse detail, and coarse-detail). Each level processes computational complexity at a manageable scale, and the graphic equalizer coordinates these segmented tasks to achieve high precision without overwhelming computational burden.
Solution Approach 2:
The patent introduces dynamic weighting parameters through the graphic equalizer that can adjust the importance of each histogram level based on search criteria. This dynamic adjustment allows the system to optimize computational complexity by focusing resources on the most critical detail levels for each specific search task, reducing overall processing time while maintaining precision.
Data Source
AI summary
A system and method for utilizing a graphic equalizer in performing image search procedures includes a histogram calculator for creating sets of histograms that represent corresponding images. A graphic equalizer is configured to select weighting parameters for performing the image search procedure. A feature detector calculates distance measures between histogram comparison pairs of a query image and subject images. The feature detector applies the weighting parameters to the distance measures to produce weighted distance measures. The feature detector next combines the weighted distance measures from the histogram comparison pairs to produce a composite distance measure for each of the subject images. The feature detector may then identify one or more of the subject images that are similar to the query image by analyzing the composite distance measures.