User-guided Image Segmentation Using K-means Clustering
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Solution Overview
Problem
Existing image segmentation methods lack efficiency in automatically selecting regions of interest, especially in digital movie production where manual intervention is time-consuming and prone to errors.
Innovation Solution
A user-guided image segmentation method that employs k-means clustering to group pixels into initial clusters, followed by re-clustering to enhance pixel connectivity, and finally classifies these clusters based on user-indicated categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image segmentation is performed by a colorist, then accurate region selection can be achieved, but the process is time-consuming and reduces productivity
Solution Approach 1:
The image is divided into multiple clusters of pixels based on color similarity, creating discrete regions that can be independently processed and classified. This segmentation approach automates the region selection process while maintaining accuracy by grouping pixels with similar characteristics together.
Solution Approach 2:
The system performs automatic image segmentation without requiring manual intervention from a colorist. The algorithm independently identifies and classifies regions of interest through automated clustering and processing, eliminating the need for time-consuming manual work while maintaining segmentation quality.
2Productivity
If fully automatic image segmentation is implemented, then productivity increases, but the system lacks adaptability to user-specific requirements
Solution Approach 1:
The system incorporates user feedback by allowing colorists to review and adjust the automatically generated cluster assignments. Users can provide corrections or modifications to the segmentation results, and this feedback is used to refine the clustering process, combining automated efficiency with user-specific adaptability.
Solution Approach 2:
The segmentation system is designed to be dynamic and adaptable, allowing the clustering parameters and processing to be adjusted based on user input and specific project requirements. The system can modify its behavior to accommodate different user preferences and image characteristics while maintaining automated processing.
3Manufacturing precision
If complex clustering algorithms are used to improve segmentation accuracy, then manufacturing precision increases, but device complexity increases
Solution Approach 1:
The complex image processing task is broken down into simpler sequential steps: initial clustering based on color similarity, followed by connectivity analysis, and finally classification. This segmentation of the algorithm into manageable stages reduces overall complexity while maintaining boundary accuracy through the systematic application of each step.
Solution Approach 2:
The patent introduces intermediate processing steps between initial clustering and final classification, including connectivity analysis and cluster refinement. These intermediary processes help bridge the gap between simple clustering and accurate segmentation, achieving high precision without requiring a single overly complex algorithm.
4Measurement precision
If manual classification of all pixel clusters is performed, then classification accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
Instead of requiring manual classification of all pixel clusters, the system performs automatic classification on the majority of clusters based on their color and connectivity characteristics. Manual intervention is only required for a small subset of ambiguous or edge cases, significantly reducing the time investment while maintaining overall classification accuracy.
Solution Approach 2:
The system automatically classifies most clusters without user intervention by analyzing cluster properties such as color similarity, connectivity, and spatial relationships. This self-service classification approach handles the bulk of the work automatically, reserving manual effort only for cases where automatic classification is uncertain.
Data Source
AI summary
A method for image segmentation includes (a) clustering, based upon k-means clustering, pixels of an image into first clusters, (b) outputting a cluster map of the first clusters (c) re-clustering the pixels into a new plurality of non-disjoint pixel-clusters, and (d) classifying the non-disjoint pixel-clusters in categories, according to a user-indicated classification. Another method for image segmentation includes (a) forming a graph with each node of the graph corresponding to a first respective non-disjoint pixel-cluster of the image and connected to each terminal of the graph and to all other nodes corresponding to other respective non-disjoint pixel-clusters that, in the image, are within a neighborhood of the first respective non-disjoint pixel-cluster, (b) setting weights of connections of the graph according to a user-indicated classification in categories respectively associated with the terminals, and (c) segmenting the image into the categories by cutting the graph based upon the weights.