Graph-Cut Object Segmentation Using Motion and Appearance Cues
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Solution Overview
Problem
Conventional segmentation systems face challenges in flexibility, accuracy, and computational efficiency when generating segmentation masks, particularly due to disjoint development of video and image segmentation techniques, reliance on large annotated datasets, and insufficient utilization of motion and appearance information, leading to overfitting and inaccuracies.
Innovation Solution
A self-supervised object discovery system that combines motion and appearance information using a neural network encoder to generate a segmentation mask through a graph-cut process, leveraging optical flow and image patch features to produce high-quality masks, and iteratively trains a segmentation model with pseudo-ground truths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks trained on large annotated datasets are used to localize objects, then segmentation accuracy is improved, but computational efficiency and processing time deteriorate
Solution Approach 1:
The patent segments the image into multiple patches and processes them independently through the neural network, then combines the results. This segmentation approach reduces the computational burden on the neural network while maintaining segmentation accuracy, as each patch can be processed more efficiently than the entire image.
Solution Approach 2:
The patent performs preliminary processing by extracting features from image patches and computing optical flow between patches before feeding them to the neural network. This preliminary action prepares the data in advance, reducing the computational load during the main segmentation process and improving overall processing speed.
2Measurement precision
If deep neural networks are used to produce high-quality object segmentation masks, then segmentation accuracy is improved, but processing time deteriorates
Solution Approach 1:
By dividing the image into patches and processing them in parallel through the neural network, the patent reduces the time required to process the entire image while maintaining high segmentation quality. The parallel processing of multiple patches significantly decreases processing time compared to processing the full image sequentially.
Solution Approach 2:
The patent processes only the necessary portions of the image (patches) rather than the entire image at once. This partial action approach allows the neural network to focus computational resources on relevant regions, reducing processing time while maintaining segmentation accuracy for the objects of interest.
3Productivity
If conventional methods are used to segment objects, then computational resources are reduced, but segmentation quality and boundary accuracy deteriorate
Solution Approach 1:
The patent changes the input parameters to the neural network by using extracted features from image patches and optical flow information rather than raw pixel data. This parameter transformation allows the neural network to work with more compact and informative data representations, achieving high segmentation quality with reduced computational resources.
Solution Approach 2:
The patent combines multiple types of information (image patch features, optical flow features, and neural network predictions) to create a composite segmentation result. This composite approach leverages the strengths of different data sources to achieve high segmentation quality while maintaining computational efficiency through feature-level processing rather than pixel-level processing.
4Adaptability or versatility
If the system processes various image/video qualities and object boundaries, then adaptability is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the input into patches, which allows the same processing pipeline to handle images and videos of varying qualities and complexities uniformly. This segmentation approach provides adaptability to different input types while maintaining consistent computational efficiency, as the patch-based processing can be adjusted to match the complexity of each specific input.
Solution Approach 2:
The patent employs dynamic processing where the neural network and graph-cut algorithm adapt to the specific characteristics of each input image or video frame. The system dynamically adjusts the number of patches, feature extraction depth, and optimization iterations based on the complexity of the input, providing flexibility across different media qualities while optimizing computational efficiency for each specific case.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide self-supervised object discovery systems that combine motion and appearance information to generate segmentation masks from a digital image or digital video and delineate one or more salient objects within the digital image/digital video. The disclosed systems utilize a neural network encoder to generate a fully connected graph based on image patches from the digital input, incorporating image patch feature and optical flow patch feature similarities to produce edge weights. The disclosed systems partition the generated graph to produce a segmentation mask. Furthermore, the disclosed systems iteratively train a segmentation network based on the segmentation mask as a pseudo-ground truth via a bootstrapped, self-training process. By utilizing both motion and appearance information to generate a bi-partitioned graph, the disclosed systems produce high-quality object segmentation masks that represent a foreground and background of digital inputs.