Patch-Based Image Retargeting With Semantic Constraints
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Solution Overview
Problem
Existing patch-based image processing techniques often lead to global inconsistencies and deformations due to the lack of effective constraints on location and geometric orientation during image manipulation, such as retargeting, which can result in artifacts that distort semantic features like straight lines and shapes.
Innovation Solution
A computer-implemented method using a patch-based optimization algorithm that restricts location and geometric orientation of patches in the second image based on user-defined or automatically identified constraints, ensuring that specific features like straight lines or regions are preserved or transformed uniformly, thereby preventing distortions and maintaining the original texture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patch-based optimization algorithm is used for image retargeting, then image manipulation flexibility is improved, but global inconsistencies and deformations occur
Solution Approach 1:
The patent changes the parameter space by introducing semantic constraints (straightness, parallelism, perpendicularity) as additional optimization parameters. These constraints are mathematically formulated and integrated into the patch-based optimization algorithm, allowing the system to simultaneously optimize for both flexibility and geometric accuracy through multi-parameter optimization.
Solution Approach 2:
The patent introduces semantic feature detectors and constraint satisfaction mechanisms as intermediary components between the patch-based optimization and the final image output. These intermediaries evaluate geometric properties and enforce constraints during the optimization process, acting as mediators that reconcile the flexibility of patch manipulation with the precision of geometric preservation.
2Reliability
If nearest neighbor patches are calculated for bidirectional similarity, then image content consistency is improved, but computational demand increases
Solution Approach 1:
The patent segments the image processing task by dividing it into distinct stages: semantic feature detection, constraint formulation, and patch-based optimization with constraint satisfaction. This segmentation allows bidirectional similarity calculation to be performed only on relevant patches that satisfy semantic constraints, reducing the overall computational burden while maintaining content consistency.
Solution Approach 2:
The patent applies partial action by calculating bidirectional similarity only for patches that meet semantic constraints rather than all patches. This selective approach performs the computationally intensive nearest neighbor search only where necessary, maintaining image content consistency in critical regions while reducing overall processing time through targeted computation.
3Manufacturing precision
If semantic constraints are applied to restrict location and geometric orientation, then feature preservation is improved, but algorithm complexity increases
Solution Approach 1:
The patent applies local quality by implementing semantic constraints locally at the patch level rather than globally across the entire image. Each patch evaluation independently checks geometric constraints (straightness, parallelism, perpendicularity) and adjusts placement accordingly. This localized approach preserves features accurately while managing algorithm complexity through modular, reusable constraint checking functions.
Data Source
AI summary
A computer-implemented method for generating a modified image includes initiating, in a computer device, a patch-based optimization algorithm for using a first image to generate a second image. The method includes identifying, in a computer device, a first feature of a first image. The identification corresponds to a constraint on the first feature for generating a second image, The constraint restricts at least one of location and geometric orientation in the second image of patches representing the first feature. The method includes identifying, using the computer device, contents for the second image by processing patches in the image in iterations of the patch-based optimization algorithm. The patch-based optimization complies with the constraint, The method includes generating the second image from the identified contents based on the iterations of the patch-based optimization algorithm. The second image includes a second feature generated to represent the first feature and complying with the constraint.