Oracle-Based Probabilistic Image Patch Matching
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Solution Overview
Problem
Conventional patch matching techniques are slow, especially in large search spaces and when allowing for patch rotation and scaling, due to high dimensionality, which hampers their efficiency in finding accurate image patches.
Innovation Solution
The implementation of an oracle-based probabilistic algorithm that uses an approximate similarity heuristic to quickly guide the search towards likely areas of the search space, reducing the need for extensive pixel-by-pixel calculations and improving convergence rates by probabilistically sampling and accepting image patches based on their similarity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional patch matching uses random sampling and sum-of-squares distance evaluations to find accurate patch matches, then measurement precision is improved, but productivity deteriorates due to slow performance in large search spaces
Solution Approach 1:
The patent segments the patch matching process into two distinct phases: (1) an oracle-based filtering phase that quickly identifies candidate patches using approximate similarity measures, and (2) a precise evaluation phase that applies sum-of-squares distance only to the filtered candidates. This segmentation allows the system to maintain measurement precision while dramatically improving productivity by avoiding exhaustive comparisons across the entire search space.
Solution Approach 2:
The patent performs preliminary filtering using an oracle-based approximate similarity measure before conducting the computationally expensive sum-of-squares distance evaluations. By pre-identifying candidate patches that are likely to be good matches, the system reduces the number of precise evaluations needed, thereby improving speed without sacrificing accuracy.
2Adaptability or versatility
If conventional patch matching allows for patch rotation and scaling to handle generalized patch match, then adaptability is improved, but productivity deteriorates due to high dimensionality of the search space
Solution Approach 1:
The patent segments the high-dimensional search space into multiple lower-dimensional subspaces, each corresponding to a specific transformation (rotation angle, scaling factor, etc.). The oracle-based filtering is applied independently within each subspace, reducing the computational complexity from evaluating all transformations simultaneously to evaluating them in manageable chunks, thereby maintaining adaptability while improving productivity.
Solution Approach 2:
The patent applies partial action by using the oracle-based approximate similarity measure to evaluate only the most promising transformations rather than exhaustively evaluating all possible transformations. This allows the system to handle generalized patch matching with rotation and scaling while maintaining practical search speeds by focusing computational resources on the most likely candidates.
3Measurement precision
If conventional patch matching scans over all image patches to compute similarity scores, then measurement precision is improved, but loss of time increases due to the large number of distance evaluations required
Solution Approach 1:
The patent extracts and removes the time-consuming sum-of-squares distance evaluation step from the main search loop, replacing it with a faster oracle-based approximate similarity measure for the majority of patches. The precise sum-of-squares evaluation is extracted and applied only to the small subset of candidate patches identified by the oracle, thereby dramatically reducing the total time required while maintaining measurement precision for the final results.
Data Source
AI summary
The present disclosure is directed toward systems and methods for image patch matching. In particular, the systems and methods described herein sample image patches to identify those image patches that match a target image patch. The systems and methods described herein probabilistically accept image patch proposals as potential matches based on an oracle. The oracle is computationally inexpensive to evaluate but more approximate than similarity heuristics. The systems and methods use the oracle to quickly guide the search to areas of the search space more likely to have a match. Once areas are identified that likely include a match, the systems and methods use a more accurate similarity function to identify patch matches.