Rank Vector Matching for Illumination-Invariant Object Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional feature vector matching methods are resource-intensive and sensitive to exact dimension values, making them inefficient for large or complex datasets, and may fail to identify similarities in objects with similar characteristics but differing values, such as images with varying illumination or audio with different volume levels.
Innovation Solution
The method constructs rank vectors based on the sequential ordering of feature vector dimensions, allowing for matching objects by comparing the order rather than the values, which reduces computational complexity and emphasizes the significance of dimension rank over actual values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional distance measures are used to compare feature vectors, then exact value differences are captured, but computational resources are excessively consumed and sensitivity to value changes increases
Solution Approach 1:
The patent transforms the feature vector comparison from value-based to rank-based by applying a monotonic transformation function. Instead of computing distance measures using actual feature values (which is computationally expensive), the system converts values to ranks and computes distance measures on the ranked data, maintaining relative ordering information while reducing computational complexity
Solution Approach 2:
The patent uses rank values as simplified proxies for the original feature vectors. Ranks are computationally inexpensive to compute and store compared to high-dimensional feature vectors with complex values, enabling efficient comparison operations while preserving the essential ordering relationships needed for matching
2Measurement precision
If exact value comparisons are performed on feature vectors, then precise distance measures are obtained, but the system becomes sensitive to irrelevant value changes such as illumination variations or volume level differences
Solution Approach 1:
The patent applies a monotonic transformation that converts feature values to ranks, effectively removing sensitivity to absolute value changes. This transformation preserves the relative ordering of features while eliminating the impact of irrelevant variations such as illumination intensity in images or volume levels in audio, making the matching more robust and reliable
Solution Approach 2:
The patent introduces rank values as an intermediary representation between the original feature vectors and the distance computation. This intermediary layer filters out irrelevant value variations while preserving the essential structural relationships, enabling reliable matching despite changes in absolute values
3Loss of information
If high-dimensional feature vectors are compared using conventional methods, then comprehensive object characteristics are analyzed, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent transforms the comparison operation from the original high-dimensional value space to a rank space. This transformation reduces computational complexity because rank-based distance measures involve simpler operations (comparing ordinal positions) rather than complex calculations on high-dimensional continuous values, while still capturing the essential relationships between features
Data Source
AI summary
Systems and methods for measuring consistency between two objects based upon a rank of object elements instead of based upon the values of those object elements. Objects being compared can be represented by d-dimension feature vectors, U and V, where each dimension includes an associated value. U and V can be converted to rank vectors, P and Q, where values of U and V dimensions are replaced by an ordered rank or a function thereof. Analysis directed to the consistency between U and V can be accomplished by determining consistency between P and Q, which can be more efficient and more accurate, particularly with regard to illumination-invariant comparisons.