Image Feature Weight Adjustment for Scale Combination
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Solution Overview
Problem
Current image processing methods face challenges in effectively combining image features of different scales to achieve comprehensive image information, leading to suboptimal results in image segmentation, classification, and object detection.
Innovation Solution
The proposed method involves determining a first image feature with at least two channels, performing weight adjustment using a weight adjustment parameter with multiple components, downsampling to obtain a second image feature, and combining these features to obtain a combined image feature, which is then used to determine an image processing result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image features of different scales are combined to achieve comprehensive image information, then image processing performance is improved, but information loss occurs during downsampling
Solution Approach 1:
The patent changes the parameter of image feature scale by performing downsampling operations to generate multiple scales of image features from a single input image. This allows the model to process and combine features at different resolutions, improving overall image processing performance while managing information loss through parameter transformation
Solution Approach 2:
The patent combines image features of different scales (first image feature and second image feature) to create a composite representation that leverages both fine-grained and coarse-grained information. This composite approach improves processing reliability by integrating complementary information from multiple scales
2Measurement precision
If weight adjustment is performed on each channel with multiple parameter components, then channel feature precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by performing weight adjustment on each channel independently with specific parameter components tailored to that channel's characteristics. This allows precise control over feature extraction for each channel while maintaining the ability to process multiple channels in parallel, managing computational complexity through structured local optimization
3Productivity
If downsampling is performed to obtain smaller size features, then computational efficiency is improved, but spatial resolution is reduced
Solution Approach 1:
The patent segments the image processing into multiple scales by downsampling the input image to generate both first image features (higher resolution) and second image features (lower resolution). This segmentation allows the model to process features at different granularities, improving computational efficiency for overview tasks while preserving spatial resolution for detailed analysis
Data Source
AI summary
An image processing method, apparatus, and device, and a storage medium are provided. The method is performed by a computing device, and includes: determining a first image feature of a first size of an input image, the first image feature having at least two channels; performing weight adjustment on each channel in the first image feature by using a first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; downsampling the adjusted first image feature to obtain a second image feature having a second size; combining the first image feature and the second image feature to obtain a combined image feature; and determining an image processing result according to the combined image feature.