Salient Content Neural Networks for Mobile Object Segmentation
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Solution Overview
Problem
Conventional digital visual media systems struggle with accurate and efficient object segmentation in digital images, particularly on mobile devices, due to limitations in classification models that require high computational resources and cannot handle real-time processing or an unlimited number of object categories.
Innovation Solution
The implementation of salient content neural networks for object segmentation, which are trained to differentiate between foreground and background pixels, allowing for efficient and accurate identification of objects in both static and real-time digital visual media on mobile devices, with the ability to handle an unlimited number of object categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification models are used for object segmentation, then object categorization can be achieved, but computational resource requirements become prohibitively high for mobile devices
Solution Approach 1:
The patent extracts and utilizes only the essential features needed for segmentation by training neural networks to identify foreground-background boundaries directly, rather than using comprehensive classification models that analyze all possible object attributes. This extraction of critical segmentation features reduces computational load while maintaining accuracy.
Solution Approach 2:
The patent employs lightweight neural network models that can be deployed on mobile devices with limited resources. These simplified models trade some complexity for efficiency, enabling segmentation functionality on devices that cannot support full-scale classification systems.
2Adaptability or versatility
If conventional classification models are used for object segmentation, then objects can be categorized into fixed categories, but the system cannot handle real-time processing or unlimited object categories
Solution Approach 1:
Instead of classifying objects into predefined categories and then segmenting them, the patent inverts the approach by directly segmenting foreground from background pixels without categorical classification. This eliminates the bottleneck of fixed category limitations and enables handling of unlimited object types in real-time.
Solution Approach 2:
The patent applies segmentation at the pixel level to divide the image into foreground and background regions directly, bypassing the need for object categorization. This pixel-based segmentation approach allows real-time processing and accommodates any number of object categories without requiring predefined class structures.
3Ease of operation
If manual tracing is used for object segmentation, then users can select individual objects, but the process requires significant time and results in inaccurate segmentation
Solution Approach 1:
The patent replaces the mechanical manual tracing process with an automated neural network-based segmentation system. The neural network automatically identifies and segments objects without requiring user interaction, eliminating the time loss associated with manual tracing while maintaining ease of operation through automatic object selection.
4Measurement precision
If conventional classification models are used for object segmentation, then objects can be segmented based on determined categories, but the systems cannot operate on mobile devices with limited memory and processing power
Solution Approach 1:
The patent employs lightweight neural network models optimized for mobile deployment, using simplified architectures that reduce memory footprint and computational requirements. These compact models enable accurate segmentation on resource-constrained mobile devices without requiring the complex infrastructure of conventional classification systems.
Solution Approach 2:
The patent modifies model parameters and architecture to suit mobile device constraints, using smaller network sizes, reduced precision representations, and optimized computational operations. These parameter changes maintain segmentation accuracy while adapting the system to operate within the memory and processing limits of mobile devices.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or more salient content neural networks. In particular, in one or more embodiments, the disclosed systems and methods train one or more salient content neural networks to efficiently identify foreground pixels in digital visual media. Moreover, in one or more embodiments, the disclosed systems and methods provide a trained salient content neural network to a mobile device, allowing the mobile device to directly select salient objects in digital visual media utilizing a trained neural network. Furthermore, in one or more embodiments, the disclosed systems and methods train and provide multiple salient content neural networks, such that mobile devices can identify objects in real-time digital visual media feeds (utilizing a first salient content neural network) and identify objects in static digital images (utilizing a second salient content neural network).