Dynamic Superpixel Count for Image Segmentation

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Solution Overview

Problem

Existing image labeling systems face inefficiencies in processing images with varying complexities, as they often require substantial computational resources and time for accurate superpixel generation, and typically fail to accurately represent contours due to inadequate user input for training prediction models.

Innovation Solution

The system allows users to specify a target quantity of superpixels for different regions of an image, enabling the generation of appropriate superpixels for simpler or complex renderings, thereby reducing computational resources and improving accuracy through user input and feedback-based training of prediction models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a substantially large quantity of superpixels are generated for images with complex renderings to accurately represent contours, then the accuracy of contour representation is improved, but the computational resources and time required increase significantly

Engineering Contradiction:

Improveaccuracy of contour representationVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts the number of superpixels generated based on the complexity of the image rendering. For simple renderings, fewer superpixels are generated, while for complex renderings, a larger quantity is produced. This parameter adaptation allows the system to maintain high contour representation accuracy for complex images while reducing computational resource consumption for simpler images.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The superpixel generation process is made dynamic by adjusting the quantity of superpixels according to the specific characteristics of each image. Rather than using a fixed large quantity for all images, the system adapts the superpixel count to match the rendering complexity, thereby optimizing the balance between accuracy and computational efficiency.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If a substantially large quantity of superpixels are generated for all images to ensure accurate contour representation, then the accuracy is improved, but the processing time increases significantly

Engineering Contradiction:

Improveaccuracy of contour representationVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system changes the parameter of superpixel quantity based on image complexity assessment. For images with simple renderings, the superpixel count is reduced, directly decreasing processing time. For images with complex renderings requiring accurate contour representation, the superpixel count is increased to maintain accuracy. This dynamic parameter adjustment resolves the time-accuracy tradeoff.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If typical existing labeling systems process images with a fixed quantity of superpixels, then the system complexity is reduced, but the system cannot efficiently handle images with varying rendering complexities

Engineering Contradiction:

Improvesystem complexityVSAvoidability to handle varying image complexities

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The labeling system incorporates dynamic adaptation by adjusting the number of superpixels generated based on the complexity of each image's rendering. This allows the system to efficiently handle a diverse range of images with varying complexities, from simple 2D shapes to complex high-definition photos, without requiring a fixed overly-complex configuration for all cases.

Inventive Principle:

Principle #15Dynamics

4Ease of manufacture

If bounding boxes are used to specify concept locations in images, then the training process is simplified, but the contours of the concept are not accurately represented

Engineering Contradiction:

Improveease of trainingVSAvoidaccuracy of contour representation

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The system transitions from using simple bounding boxes to generating segmented superpixels that closely follow the contours of concepts in images. This segmentation approach maintains ease of training through automated processes while significantly improving the accuracy of contour representation, as superpixels are generated to match the actual boundaries of objects rather than enclosing them in loose rectangular boxes.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11514693B2Model-based image labeling and/or segmentation

Publication Date: 2022.11.29 NEBIUS BV

AI summary

In some embodiments, reduction of computational resource usage related to image labeling and/or segmentation may be facilitated. In some embodiments, a collection of images may be used to train one or more prediction models. Based on a presentation of an image on a user interface, an indication of a target quantity of superpixels for the image may be obtained. The image may be provided to a first prediction model to cause the prediction model to predict a quantity of superpixels for the image. The target quantity of superpixels may be provided to the first model to update the first model's configurations based on (i) the predicted quantity and (ii) the target quantity. A set of superpixels may be generated for the image based on the target quantity, and segmentation information related to the superpixels set may be provided to a second prediction model to update the second model's configurations.