Content-Aware Image Selection via Object Metadata
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Solution Overview
Problem
Current image editing tools require users to manually select areas based on coordinates and pixel values, which can be time-consuming and difficult, especially when dealing with complex shapes like natural objects.
Innovation Solution
A content-aware selection system that allows users to select image areas using labels or tags, utilizing object recognition and metadata to automatically generate selection boundaries around identified objects, leveraging neural networks and lexical dictionaries for accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually select areas based on coordinates and pixel values, then selection precision can be achieved, but the operation time and complexity increase significantly
Solution Approach 1:
The system performs preliminary object recognition and metadata generation automatically before the user needs to make a selection. By pre-processing the image to identify objects and generate bounding box metadata, the system eliminates the need for users to manually define selection areas, thus reducing operation time while maintaining precision through automated coordinate calculation.
Solution Approach 2:
The patent replaces the manual mechanical process of drawing selection boundaries with automated computer vision algorithms. Neural networks and object recognition systems automatically detect objects and generate precise selection areas, substituting human manual operations with intelligent automated systems that achieve both speed and accuracy.
2Adaptability or versatility
If traditional pixel-based selection tools are used, then selection flexibility is maintained, but the ease of operation deteriorates when dealing with complex shapes
Solution Approach 1:
The system introduces metadata as an intermediary layer between the user and the pixel data. Instead of directly manipulating pixels, users interact with high-level object descriptions and bounding box metadata, which then automatically translate into precise pixel-based selections. This intermediary metadata structure simplifies the user interface while maintaining full control over the underlying pixel selection.
3Manufacturing precision
If manual selection drawing is required, then precise control over selection boundaries is achieved, but the device complexity and user effort increase
Solution Approach 1:
The system performs self-service by automatically generating selection boundaries through object recognition algorithms. The neural network independently analyzes the image, identifies objects, and creates precise bounding boxes without requiring user intervention for boundary drawing. This self-automating capability reduces the effective complexity the user must manage while maintaining high precision through sophisticated algorithms.
Data Source
AI summary
An image editing program can include a content-aware selection system. The content-aware selection system can enable a user to select an area of an image using a label or a tag that identifies object in the image, rather than having to make a selection area based on coordinates and/or pixel values. The program can receive a digital image and metadata that describes an object in the image. The program can further receive a label, and can determine from the metadata that the label is associated with the object. The program can then select a bounding box for the object, and identify in the bounding box, pixels that represent the object. The program can then output a selection area that surrounds the pixels.