Image Modification Using Description Parameter Clustering
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Solution Overview
Problem
Existing image modification methods in electronic devices do not accurately reflect user-provided text descriptions, as they apply pre-defined filters uniformly without considering the specific text description, leading to mismatches between the image and its description.
Innovation Solution
An electronic device determines description parameters associated with an image, clusters relevant portions, generates descriptors, and modifies the image based on these parameters using an image processing engine and machine learning models to align the image with the user's text description, employing back-propagation to refine the modifications until a loss threshold is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-defined filters are applied to modify an image, then the image modification process is simplified and can be performed automatically, but the modification does not accurately reflect the user's text description and may mismatch with the intended visual attributes
Solution Approach 1:
The image is divided into multiple clusters based on visual features and text description keywords. Each cluster represents a specific region or object that can be independently modified according to its corresponding description parameter, allowing precise control over different parts of the image while maintaining overall coherence
Solution Approach 2:
Different modification parameters are applied to different clusters within the image based on their corresponding text descriptions. For example, brightness adjustments are applied specifically to clusters associated with 'bright' keywords, while other regions remain unchanged or receive different adjustments, ensuring local precision rather than uniform global modification
2Productivity
If pre-defined filters modify the entire image uniformly, then the implementation is straightforward and computationally efficient, but the text description provided by the user is not considered in the modification process
Solution Approach 1:
The system uses a loss function to compare the modified image clusters with the original image and the text description. The loss value provides feedback on how well the current modification parameters align with the user's description, allowing iterative optimization of modification parameters to achieve better alignment while maintaining computational efficiency
Solution Approach 2:
The system performs preliminary actions by determining description parameters from the text description and clustering the image based on these parameters before applying modifications. This preliminary organization allows the system to efficiently target specific regions for modification rather than uniformly processing the entire image, balancing adaptability with computational efficiency
3Measurement precision
If the user manually selects and applies filters to modify specific portions of the image, then the alignment with text description can be improved, but the process becomes more tedious and complex for the user
Solution Approach 1:
The system automatically performs the entire workflow of analyzing the text description, clustering the image, determining modification parameters for each cluster, and applying the modifications. The user simply provides the text description and selects a filter type, while the system handles the complex tasks of precise region identification and parameter optimization, achieving high alignment precision without increasing user operation complexity
Data Source
AI summary
An electronic device and a method are provided for description parameter based modification of images. The method includes determining a description parameter associated with an image; determining a cluster including a portion related to the description parameter of the image to be modified; and modifying the portion of the image based on the description parameter.