Feature Map Encoding via PCA Transform for Image Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image encoding/decoding technologies struggle to efficiently compress and recognize images, especially when considering both human vision and machine vision applications.
Innovation Solution
The method involves generating a feature map from a multi-level feature group and performing PCA transform on the feature map to reduce data volume during encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-level feature groups are directly encoded, then recognition accuracy is maintained, but data volume and encoding complexity increase
Solution Approach 1:
The multi-level feature group is segmented into multiple feature maps corresponding to different resolution levels. Each feature map is processed independently through PCA transform, allowing selective compression at different levels while preserving critical recognition features.
Solution Approach 2:
PCA transform is applied to change the parameter representation of feature maps from pixel-based to component-based. This transformation reduces the dimensionality of each feature map by identifying and retaining only the most significant principal components, thereby reducing data volume while maintaining recognition accuracy.
2Loss of information
If multi-level feature groups are directly encoded, then all feature information is preserved, but encoding complexity and processing time increase
Solution Approach 1:
The encoding process is segmented into multiple independent stages, one for each feature map level. This segmentation allows the encoder to process each resolution level separately with optimized complexity, avoiding the need to handle all feature information simultaneously in a single complex operation.
Solution Approach 2:
The application of PCA transform changes the parameter space from high-dimensional pixel values to lower-dimensional principal components. This parameter transformation simplifies the encoding complexity by reducing the number of parameters that need to be processed and transmitted, while the segmented approach ensures that feature information from all levels is preserved through selective component retention.
3Quantity of substance
If PCA transform is applied to reduce data volume, then compression efficiency improves, but processing overhead increases
Solution Approach 1:
The PCA transform operation is segmented and applied independently to each feature map rather than to the entire multi-level feature group at once. This segmentation enables parallel processing of different resolution levels, reducing the total processing time while achieving the same overall compression efficiency.
Solution Approach 2:
Instead of applying full PCA transform to all feature maps with maximum components, the method uses partial action by selecting only the top k principal components for each feature map based on their explained variance. This partial retention of components achieves sufficient compression efficiency while significantly reducing the processing time compared to using all components.
Data Source
AI summary
A feature map encoding method according to the present disclosure may include generating a feature map from a multi-level feature group; and performing PCA (Picture Component Analysis) transform for the feature map. Here, generating the feature map comprises reshaping the multi-level feature group; and generating the feature map by merging a plurality of base units generated by the reshape.