Semantic Image Segmentation via Patch-Based Generative Feature Extraction
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Solution Overview
Problem
Current image classification techniques are unreliable for locating objects within images, as they primarily rely on low-level features like color and texture, leading to inaccurate object localization and segmentation.
Innovation Solution
An automated image processing method that extracts patches from an input image, computes high-level features using generative models, and assigns object class labels to pixels based on relevance scores, enabling semantic class-based segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-level features (color, texture) are used for object localization, then the processing speed is fast, but the localization accuracy is poor
Solution Approach 1:
The patent segments the image processing task into multiple levels: low-level feature extraction (color, texture), mid-level patch-based processing, and high-level semantic classification. This hierarchical segmentation allows the system to maintain fast low-level processing while improving accuracy through higher-level analysis.
Solution Approach 2:
The patent introduces patch-based representations as an intermediary between low-level pixel data and high-level semantic concepts. Patches serve as intermediate units that capture local contextual information, bridging the gap between simple color/texture features and complex object classification.
2Measurement precision
If patch-based classification with high-level features is used, then the object localization accuracy is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary low-level feature extraction and patch formation before high-level classification. By preparing patch representations in advance and using them for both classification and localization, the system avoids redundant processing while maintaining accuracy.
Solution Approach 2:
The patent merges classification and localization tasks into a unified framework. The same patch-based features and classifiers used for object class identification also provide localization information, eliminating the need for separate processing pipelines and reducing overall processing time.
3Manufacturing precision
If semantic class labels are assigned to pixels, then the image segmentation quality is improved, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by using patch-based processing where each patch is classified independently based on its local features. This allows high-resolution semantic segmentation without requiring global analysis of the entire image, reducing computational complexity while maintaining precision.
Solution Approach 2:
The patent transitions from pixel-level processing to patch-level processing, adding a spatial dimension to the analysis. By operating on patches rather than individual pixels, the system achieves better segmentation precision with reduced computational burden compared to full pixel-wise classification.
Data Source
AI summary
An automated image processing system and method are provided for class-based segmentation of a digital image. The method includes extracting a plurality of patches of an input image. For each patch, at least one feature is extracted. The feature may be a high level feature which is derived from the application of a generative model to a representation of low level feature(s) of the patch. For each patch, and for at least one object class from a set of object classes, a relevance score for the patch, based on the at least one feature, is computed. For at least some or all of the pixels of the image, a relevance score for the at least one object class based on the patch scores is computed. An object class is assigned to each of the pixels based on the computed relevance score for the at least one object class, allowing the image to be segmented and the segments labeled, based on object class.