Gaussian Mixture Model for Digital Image Patch Matching
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Solution Overview
Problem
Conventional digital image editing systems are inefficient due to high memory and processing demands, slowness, inflexibility, and inaccuracy in identifying and modifying digital image patch matches, particularly when dealing with multiple transformations and high-dimensionality analysis.
Innovation Solution
The use of a Gaussian mixture model to identify and generate patch matches, which reflects probability distributions of visual patterns within a digital image, allowing for efficient and accurate modification of target regions by analyzing sample patches and their transformations, and sampling target matching portions for filling holes or blemishes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional binning method is used to group digital image data into bins for each image patch, then patch matches can be identified, but the amount of memory required to store and analyze bin data becomes excessively large
Solution Approach 1:
The patent extracts only the essential features needed for patch matching from the complete image data. Instead of binning all pixel data, the system extracts representative features (such as color histograms, texture descriptors, or other compact representations) and stores only these extracted features in bins, dramatically reducing memory requirements while preserving matching capability
Solution Approach 2:
The patent changes the parameter representation from storing complete pixel data to storing compressed feature vectors. By transforming the data representation from high-dimensional pixel values to lower-dimensional feature spaces, the system maintains patch match identification accuracy while significantly reducing the quantity of data that must be stored and processed
2Measurement precision
If conventional systems analyze digital images at granular basis to identify patch matches, then matching accuracy is achieved, but the processing time becomes excessively long
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing feature representations of image patches before the actual matching operation. The system pre-extracts features from all patches and organizes them in bins in advance, so that during the matching phase, only simple comparisons of pre-computed features are needed rather than re-analyzing raw pixel data, significantly reducing processing time
Solution Approach 2:
The patent segments the image into discrete patches and processes each patch independently to extract features. This segmentation allows parallel processing of multiple patches and enables the system to work with smaller, manageable units rather than analyzing the entire image at once, reducing overall processing time while maintaining accuracy
3Adaptability or versatility
If binning resolution is increased to support higher-dimensionality analysis of multiple transformations, then analysis capability is improved, but binning data becomes too large to support
Solution Approach 1:
The patent addresses high-dimensionality transformation analysis by introducing a new dimensional organization in the feature space. Instead of creating separate bins for each transformation type, the system extends the binning structure to include transformation parameters as additional dimensions, allowing efficient storage and querying of multi-transformation matches without exponentially increasing data size
Solution Approach 2:
The patent changes how transformation data is represented by using compact parameter encodings instead of storing complete transformation matrices or high-resolution bin data. By representing transformations with reduced parameter sets (such as using log-scale representations or compressed feature vectors), the system supports high-dimensionality analysis with manageable data sizes
4Measurement precision
If conventional binning approaches are used to identify patch matches, then translation matching is achieved, but interactions and correlations between bins are not captured
Solution Approach 1:
The patent incorporates feedback mechanisms where the matching process iteratively refines results by considering correlations between bins. The system uses the matching outcomes from one bin to inform and adjust the matching process in related bins, capturing contextual relationships and improving overall matching accuracy through iterative feedback loops
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.