Multi-Scale Superpixel Labeling for Rare Object Detection
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Solution Overview
Problem
Existing scene parsing techniques fail to accurately classify or label rare objects in images, such as boats and people, due to limitations in identifying and assigning appropriate labels to pixels in image scenes.
Innovation Solution
The method involves using multi-scale superpixel sets to partition input images, comparing descriptors of superpixels with reference superpixels from labeled images, and assigning labels based on similarity and spatial context, with additional techniques for expanding reference data on rare classes and incorporating spatial context information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing scene parsing techniques are used to classify pixels in images, then the process can be automated, but the accuracy of classifying rare objects (such as boats and people) deteriorates
Solution Approach 1:
The patent segments the image into superpixels at multiple scales, creating a hierarchical representation where each superpixel is a homogeneous region. This segmentation allows the system to process and classify rare objects more accurately by examining them at appropriate scales without being overwhelmed by the entire image complexity.
Solution Approach 2:
The patent introduces a multi-scale dimension by generating superpixel sets at different resolution levels. This additional dimension of analysis enables the system to capture both fine-grained details of rare objects and broader contextual information, significantly improving classification accuracy while maintaining automation.
2Measurement precision
If multi-scale superpixel sets are used to partition images, then the accuracy of object labeling improves, but the computational complexity increases
Solution Approach 1:
By segmenting the image into superpixels at multiple scales, the patent reduces the computational complexity of analyzing rare objects. Instead of processing every pixel individually, the system works with a reduced set of superpixel representations, making the enhanced accuracy achievable at lower computational cost.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. Common objects are processed at coarser scales while rare objects are examined at finer scales, optimizing the balance between accuracy and computational complexity by not uniformly applying high-complexity processing to the entire image.
3Measurement precision
If reference data is expanded to include more rare object classes, then the detection of rare objects improves, but the size of the reference database increases
Solution Approach 1:
The patent segments the reference database by object rarity and processes different classes at different scales. Rare objects are identified and processed separately from common objects, allowing the system to expand reference data for rare classes without proportionally increasing the entire database size or processing burden.
Solution Approach 2:
The patent applies different reference data strategies to different object classes. Common objects use standard reference data while rare objects receive enhanced reference data specifically tailored to their detection needs, optimizing the balance between detection accuracy and database size.
Data Source
AI summary
Disclosed are various embodiments labeling objects using multi-scale partitioning, rare class expansion, and/or spatial context techniques. An input image may be partitioned using different scale values to produce a different set of superpixels for each of the different scale values. Potential object labels for superpixels in each different set of superpixels of the input image may be assessed by comparing descriptors of the superpixels in each different set of superpixels of the input image with descriptors of reference superpixels in labeled reference images. An object label may then be assigned for a pixel of the input image based at least in part on the assessing of the potential object labels.