1D CNN Polyline and Polygon Encoding for Efficient Feature Extraction
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Solution Overview
Problem
Existing machine learning techniques, such as multi-layer perceptrons (MLPs) and 2D/3D convolutional neural networks (CNNs), are inefficient in extracting features from polylines and polygons due to their inability to utilize geometrical and spatial properties, requiring large computational resources and memory, and needing extensive training data.
Innovation Solution
A one-dimensional (1D) CNN architecture that processes multidimensional polylines and polygons by exploiting their geometrical properties, using a channel matrix representation and 1D convolutional layers to efficiently extract features, reducing computational and memory requirements while improving sample efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-layer perceptrons (MLPs) and 2D/3D convolutional neural networks (CNNs) are used to process polylines and polygons, then the models can perform classification and recognition tasks, but the computational resources and memory requirements become excessively large
Solution Approach 1:
The patent segments the coordinate data of polylines and polygons into multiple channels, where each channel represents a specific coordinate dimension (e.g., x-coordinates, y-coordinates, z-coordinates). This segmentation allows the 1D CNN to process each coordinate channel separately through convolutional operations, significantly reducing computational complexity compared to treating all coordinates as a single high-dimensional input for MLPs or 2D/3D CNNs.
Solution Approach 2:
The patent transforms the spatial coordinate data into a temporal sequence structure suitable for 1D CNN processing. By organizing coordinates as sequences along the polyline/polygon path and applying 1D convolutional filters, the model efficiently captures spatial patterns while reducing the dimensional burden. This dimensionality transformation enables the use of 1D CNNs instead of computationally heavier 2D/3D CNNs or MLPs.
2Reliability
If multi-layer perceptrons (MLPs) and 2D/3D convolutional neural networks (CNNs) are used to extract features from polylines and polygons, then the models can perform recognition tasks, but the memory requirements become excessively large
Solution Approach 1:
The patent segments coordinate information into multiple 1D channels representing different spatial dimensions. Each channel is processed independently by the 1D CNN, which requires minimal memory compared to storing and processing high-dimensional coordinate matrices. This segmentation strategy dramatically reduces memory requirements while preserving the geometric structure necessary for effective feature extraction.
Solution Approach 2:
The patent reorganizes spatial coordinate data into a 1D sequential format that can be efficiently processed by 1D CNNs. This dimensionality change transforms the data structure from high-dimensional spatial coordinates to 1D temporal sequences, enabling memory-efficient processing while maintaining the ability to extract meaningful geometric features for recognition tasks.
3Reliability
If multi-layer perceptrons (MLPs) and 2D/3D convolutional neural networks (CNNs) are used for processing polylines and polygons, then the models can perform classification tasks, but extensive training data is required
Solution Approach 1:
The patent segments coordinate data into meaningful 1D channels that preserve geometric relationships. This segmentation enables the 1D CNN to learn efficient feature representations with fewer parameters, reducing the risk of overfitting and decreasing the amount of training data required compared to MLPs or 2D/3D CNNs that require大量 training samples to learn complex spatial patterns.
Solution Approach 2:
The patent transforms spatial coordinate information into 1D sequential data suitable for 1D CNN processing. This dimensionality transformation allows the model to capture spatial patterns through 1D convolutional operations, achieving effective classification performance with reduced training data requirements compared to traditional approaches that rely on extensive datasets to learn spatial relationships.
4Use of energy by moving object
If 1D CNN architecture is used to process multidimensional polylines and polygons, then computational resources and memory requirements are reduced, but the model must effectively exploit geometrical and spatial properties
Solution Approach 1:
The patent segments coordinate data into multiple 1D channels, each representing a specific coordinate dimension. This segmentation simplifies the processing architecture by enabling independent 1D convolutional operations on each channel, reducing computational complexity while preserving geometric structure. The segmented approach avoids the need for complex 2D/3D convolutional kernels or sophisticated MLP architectures.
Solution Approach 2:
The patent transforms spatial coordinate data into 1D sequential format, enabling the use of computationally efficient 1D CNNs. This dimensionality change simplifies the overall architecture by replacing complex spatial processing mechanisms with 1D convolutional operations that are easier to implement and require fewer computational resources, while still effectively capturing geometric and spatial properties through the sequential structure.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for representing polylines and polygons. A method generally includes obtaining a ordered set of points that represent a polyline or a polygon in a multidimensional space; forming two or more channels from the ordered set of points, each channel has a respective set of coordinate values that corresponds to a respective coordinate direction in the multidimensional space; inputting the two or more channels into a one-dimensional convolutional neural network (1D CNN); and obtaining, as output from the 1D CNN, a feature vector representation of the polyline or polygon.