Featureless Image Categorization With Multi-Scale Blur Filtering
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Solution Overview
Problem
Existing image categorization and recognition systems struggle with images having amorphous features, such as fire, snow, and grassy fields, as they fail to capture these features effectively, leading to mis-characterizations and mis-categorizations, especially in computer-generated imagery like heat maps.
Innovation Solution
An unsupervised learning system using multiple low pass filters to process images, generating progressively blurry versions, and employing hash values to match and categorize images based on known resiliency statuses, reducing manual input and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image classification algorithms focus on distinct features and boundaries, then recognition accuracy for objects with clear features is improved, but performance degrades on images with amorphous features like fire, snow, water, and grassy fields
Solution Approach 1:
The patent applies parameter changes by transforming images through multiple low pass filters with different cutoff frequencies, converting the image from spatial frequency domain to a filtered version that captures amorphous patterns. This parameter transformation enables the system to recognize amorphous features that traditional algorithms miss, resolving the contradiction between accuracy for distinct features and adaptability for amorphous features.
Solution Approach 2:
The patent introduces a new dimension by adding filtered versions of images at different levels of blur to the feature space. Instead of relying solely on original sharp images, the system creates multiple dimensional representations (original image + filtered images) that capture both distinct features and amorphous patterns, enabling comprehensive recognition across different image types.
2Adaptability or versatility
If multiple low pass filters are applied to generate progressively blurry versions of images, then ability to recognize amorphous features is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing filtered versions of recorded images in a database during the training phase. When processing new images, the system only needs to compare against these pre-processed references rather than applying multiple filters in real-time, significantly reducing computational complexity while maintaining the ability to recognize amorphous features.
Solution Approach 2:
The patent uses copying by creating a database of pre-filtered image versions that serve as references. Instead of repeatedly applying complex filtering operations to new images, the system copies and compares against stored filtered versions, reducing the computational burden during inference while preserving recognition accuracy for amorphous features.
3Productivity
If traditional image classification algorithms are used, then processing speed is maintained, but mis-characterization and mis-categorization errors increase for amorphous features
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing filtered versions of recorded images in a database during the training phase. When processing new images, the system only needs to compare against these pre-processed references rather than applying multiple filters in real-time, significantly reducing computational complexity while maintaining the ability to recognize amorphous features.
Solution Approach 2:
The patent uses an intermediary approach by introducing filtered image versions as intermediate representations between the input image and the final classification decision. These filtered versions serve as mediators that capture amorphous patterns, enabling more accurate categorization without requiring the system to process every detail of the original image, thus maintaining speed while improving reliability.
Data Source
AI summary
Systems and methods are described for image categorization and recognition using unsupervised learning. The disclosure is configured to receive an unseen image; implement a first filter on the unseen image to generate a first version of the unseen image; implement a second filter on the first version of the unseen image to generate a second version of the unseen image; determine a subset of the second version of the recorded images that matches the second version of the unseen image; retrieve a subset of first version of recorded images associated with the subset of second version of recorded images; and determine a first recorded image from the subset of the first version of the recorded images that matches the first version of the unseen image. Thereafter, retrieve a first set of responsive actions associated with the first recorded image and provide the first set of responsive actions to the device.