Geometric Moments for Shape-Aware Deep Network Classification
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Solution Overview
Problem
Current machine learning models, particularly Convolutional Neural Networks (CNNs), face challenges in effectively capturing shape information due to their reliance on texture features and limited spatial extent, leading to suboptimal performance in image classification tasks and lack of interpretability.
Innovation Solution
The Deep Geometric Moment (DGM) framework trains networks to generate coordinate bases and affine transformation parameters, making features geometrically invariant and improving shape awareness by using geometric moments for image classification, which allows for better generalization and interpretability without relying on pooling layers or reducing spatial dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Convolutional Neural Networks (CNNs) are used for image classification, then the model can process visual imagery effectively, but the model lacks adequate shape awareness and interpretability due to reliance on texture features and limited spatial extent
Solution Approach 1:
The patent introduces geometric moment computations as an additional dimensional approach to feature extraction. Instead of relying solely on traditional CNN texture-based features, the system computes geometric moments (including affine invariant moments) that capture shape properties in a different feature space, thereby recovering shape information lost in conventional CNN processing
Solution Approach 2:
The patent creates a composite feature representation by combining traditional CNN extracted features with geometric moment features. This composite approach merges the strengths of both methods: CNNs provide robust texture and pattern recognition while geometric moments provide explicit shape awareness and interpretability, resulting in a more comprehensive feature set for classification
2Reliability
If standard CNN architectures are used, then the model achieves reasonable classification accuracy, but the model lacks interpretability and shape awareness
Solution Approach 1:
The patent introduces geometric moment computations as an intermediary layer between raw image input and final classification. This intermediary explicitly processes and highlights shape-relevant information through moment calculations, making the model's decision-making process more interpretable by providing a clear shape-aware feature representation that can be independently analyzed
3Productivity
If pooling layers are used to reduce spatial dimensions, then computational efficiency improves, but shape information and spatial relationships are degraded
Solution Approach 1:
The patent substitutes the mechanical pooling operation with geometric moment computations. Instead of using pooling layers that mechanically downsample and lose spatial information, the system computes geometric moments that mathematically capture shape properties without requiring spatial downsampling, thereby preserving shape information while maintaining computational efficiency through the properties of moment invariance
Data Source
AI summary
A Deep Geometric Moment framework (DGM framework) may be trained to perform an image classification task using a training dataset having a plurality of images. Training the DGM framework may include generating 2D coordinate grids for the plurality of images and computing coordinate bases. The training may further include extracting image features for the plurality of images and computing geometric moments for the image features. Training the DGM framework may include generating predicted affine transformation parameters to transform the 2D coordinate bases into a transformed coordinate grid and generating new coordinate bases and new geometric moments learned from the image features to reconstruct the plurality of images using the transformed coordinate grid. The DGM framework may output an Artificial Intelligence model (AI model) trained to perform the image classification task for an input image.