Image Object Segmentation via Superpixel Grouping
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Solution Overview
Problem
Current image segmentation methods require extensive technical skills and user interaction to differentiate between background and foreground in images, making applications like image editing and object manipulation cumbersome.
Innovation Solution
A method that groups image pixels into clusters based on similarity, allowing for automatic segmentation of objects by determining representative descriptors and iteratively grouping pixels to form dissimilar sets, which can then be used to segment objects based on user selection with minimal interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used, then segmentation accuracy can be achieved, but extensive user interaction and technical skills are required
Solution Approach 1:
The system performs automatic segmentation by computing superpixels and automatically identifying object boundaries through analysis of boundary strength and continuity, enabling the algorithm to segment objects without requiring extensive user interaction or manual adjustment
Solution Approach 2:
The system pre-computes superpixels and boundary strength maps before final segmentation, preparing the image data in advance with meaningful structures that facilitate automatic object identification and reduce the need for user intervention during the segmentation process
2Measurement precision
If traditional image segmentation methods are used, then object separation can be achieved, but considerable user interaction is required
Solution Approach 1:
The algorithm automatically identifies objects by analyzing boundary strength and continuity across superpixels, performing the segmentation task autonomously without requiring users to manually trace or select objects, thereby eliminating time-consuming user interaction
Solution Approach 2:
The system uses boundary strength feedback to iteratively refine object boundaries by evaluating the continuity and strength of edges between adjacent superpixels, automatically adjusting segmentation results based on the computed boundary characteristics
3Measurement precision
If precise object selection is required, then segmentation accuracy improves, but the complexity of operation increases
Solution Approach 1:
The system divides the image into superpixels first, creating manageable atomic units that simplify subsequent object identification. This hierarchical segmentation approach reduces operational complexity by working with grouped pixels rather than individual pixels, while maintaining segmentation accuracy through the preservation of boundary information
Solution Approach 2:
Boundary strength maps serve as an intermediary representation that bridges the gap between simple superpixel grouping and accurate object boundaries. This intermediate structure encodes boundary information that guides automatic object identification without requiring complex user input or operations
Data Source
AI summary
In an example embodiment a method, apparatus and computer program product are provided. The method includes grouping pixels of an image into a plurality of pixel groups. The method includes determining representative descriptors for the plurality of pixel groups and further grouping the plurality of pixel groups into a set of pixel groups based at least on similarity between the representative descriptors for the plurality of pixel groups. The method also includes receiving a selection of at least one region of the image. The method includes segmenting at least one object of the image based at least on an overlap between the at least one region and the set of pixel groups, wherein the at least one object is associated with one or more pixel groups of the set of pixel groups that overlap with the at least one region.