Computing Mahalanobis Distance for Images Using MATLAB
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This MATLAB implementation computes Mahalanobis distance for image comparison. Mahalanobis distance serves as a statistical measure to evaluate similarity between probability distributions, making it particularly valuable for quantifying differences between images. The calculation can incorporate various image characteristics such as grayscale values, color distributions, and texture features. Key implementation aspects include: preprocessing images to extract feature vectors, computing covariance matrices to account for feature correlations, and applying the distance formula D² = (x - y)ᵀ Σ⁻¹ (x - y) where x and y represent feature vectors and Σ is the covariance matrix. The resulting distance metric provides fundamental support for image processing and recognition applications by offering a scale-invariant similarity measure that considers dataset covariance structure.
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