Document Classification via Keypoint-Feature Fusion
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Solution Overview
Problem
Conventional document classification methods exhibit high computational complexity and dependence on training data availability, limiting their effectiveness in accurately classifying documents based on visual features.
Innovation Solution
The method involves detecting keypoints or visual words in document images, generating keypoint or visual word vectors, combining these with feature maps, transforming them using a predefined mapping scheme, and estimating importance factors to classify documents using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional document classification methods are used, then training data availability is required, but computational complexity increases and classification accuracy is limited
Solution Approach 1:
The patent segments the document image into multiple patches and extracts keypoints from each patch independently. This segmentation approach reduces the computational complexity of processing entire documents while maintaining classification accuracy by focusing on local key features that are then aggregated for overall document classification.
Solution Approach 2:
The patent extracts keypoints and visual words from document patches, separating the essential discriminative features from the rest of the image data. This extraction process reduces computational complexity by working with compact feature representations rather than full-resolution images, while improving classification accuracy through focused feature analysis.
2Productivity
If conventional document classification methods are used, then high computational complexity is incurred, but processing speed decreases
Solution Approach 1:
By dividing documents into patches and processing them independently with keypoint detection, the patent enables parallel processing that increases productivity. The segmentation allows multiple patches to be processed simultaneously, reducing overall processing time while maintaining lower computational complexity per patch compared to processing entire documents.
Solution Approach 2:
The patent transforms images into keypoint representations and visual word vectors, changing the parameter space from pixel-level data to feature-level data. This parameter transformation reduces computational complexity and enables faster processing while maintaining or improving classification accuracy through the use of importance factors that highlight discriminative features.
3Adaptability or versatility
If training data-dependent methods are used, then classification accuracy is limited, but adaptability to new document types decreases
Solution Approach 1:
The patent employs self-supervised learning where the model learns from the structure and patterns within the document images themselves without requiring extensive labeled training data. The keypoint detection and visual word extraction processes identify inherent features that enable the system to adapt to new document types while maintaining high classification accuracy through unsupervised feature learning.
Solution Approach 2:
The patent creates a universal feature extraction framework that works across different document types by detecting keypoints and visual words that are characteristic of various document formats. This multi-functional approach enables the same processing pipeline to handle diverse document types accurately without requiring type-specific training data, thereby improving both adaptability and accuracy.
Data Source
AI summary
An example method of document classification comprises: detecting a set of keypoints in an input image; generating a set of keypoint vectors, wherein each keypoint vector of the set of keypoint vectors is associated with a corresponding keypoint of the set of keypoints; extracting a feature map from the input image; producing a combination of the set of keypoint vectors with the feature map; transforming the combination into a set of keypoint mapping vectors according to a predefined mapping scheme; estimating, based on the set of keypoint mapping vectors, a plurality of importance factors associated with the set of keypoints; and classifying the input image based on the set of keypoints and the plurality of importance factors.