Confidence Weighted Pooling for Color Constancy Accuracy
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Solution Overview
Problem
Current computer vision algorithms face challenges in accurately removing color casts from images due to ambiguity in local estimates, particularly in patches with little semantic context, leading to noisy data and diminished accuracy in color constancy estimation.
Innovation Solution
A fully convolutional network (FCN) is employed, where patches in an input image differ in influence over color constancy estimation, with confidence weights integrated into a pooling layer to distinguish between useful and noisy data, enabling end-to-end training and faster computation by considering all patches simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patch-based CNN approaches are used for color constancy estimation, then local color estimates can be obtained, but ambiguous patches with little semantic context inject noise and diminish estimation accuracy
Solution Approach 1:
The patent applies local quality by assigning different confidence weights to different patches based on their semantic content and reliability. Informative patches (those with clear semantic context) receive higher weights, while ambiguous patches receive lower weights. This is implemented through the confidence-weighted pooling operation that aggregates local color estimates with patch-specific confidence scores, thereby improving overall estimation accuracy by selectively emphasizing reliable local measurements.
2Productivity
If traditional CNN approaches process patches sequentially, then individual patch estimates can be computed, but the process is computationally slower and cannot effectively compare patch usefulness
Solution Approach 1:
The patent merges all patch processing into a single fully convolutional network that operates on the entire image simultaneously. Instead of processing patches sequentially as in traditional CNNs, the FCN computes color estimates for all patches in parallel and performs confidence-weighted pooling across all patches in one operation. This approach maintains all patch relationship information and significantly improves computational efficiency by eliminating sequential processing bottlenecks.
3Device complexity
If all patches are treated equally in color constancy estimation, then the process is simpler, but ambiguous patches diminish the influence of more valuable patches
Solution Approach 1:
The patent changes the parameter of patch weighting from uniform (all patches equal) to variable (patches weighted by confidence). The confidence-weighted pooling operation introduces a new parameter dimension - the confidence score - that modulates the contribution of each patch to the final color estimate. This parameter change allows the system to maintain simplicity in the overall architecture while dramatically improving accuracy by adapting weights based on patch quality and semantic informativeness.
Data Source
AI summary
Disclosed in some examples, are methods, systems, and machine readable mediums that correct image color casts by utilizing a fully convolutional network (FCN), where the patches in an input image may differ in influence over the color constancy estimation. This influence is formulated as a confidence weight that reflects the value of a patch for inferring the illumination color. The confidence weights are integrated into a novel pooling layer where they are applied to local patch estimates in determining a global color constancy result.