Saliency-Guided Background Generation for Object-Focused Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional background generation techniques for online publications fail to consider visual saliency and extensive contextual data, resulting in distracting background images that do not highlight the object's features, and manual generation is time-consuming and tedious.
Innovation Solution
A background generation system that leverages machine learning models to aggregate user data, image tags, and textual information from online publications, using a knowledge graph to generate background images that meet a visual saliency threshold, ensuring relevance to the object and user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional background generation techniques are used, then the process is simple, but the generated background images are distracting and do not highlight the object's features
Solution Approach 1:
The system segments the background generation process into multiple specialized components: a saliency detection module that identifies important regions, a generative model that creates background content, and a blending module that combines them. This segmentation allows each component to focus on a specific task, improving overall background image quality while managing complexity through modular design.
Solution Approach 2:
The patent introduces a saliency map as an intermediary element between the original image and the generated background. This saliency map guides the generative model to preserve important object features while generating appropriate background content, thereby improving background image quality without requiring the system to be overly complex.
2Productivity
If manual background generation is used, then the background images can be customized, but the process is time-consuming and tedious
Solution Approach 1:
The system implements self-service automation where the background generation process operates autonomously without manual intervention. The saliency detection automatically identifies important regions, the generative model automatically creates appropriate backgrounds, and the blending process automatically combines them, eliminating time-consuming manual work while maintaining customized background quality.
Solution Approach 2:
The patent replaces the mechanical manual process of background generation with an automated computational system. Instead of manually designing and editing backgrounds, the system uses machine learning models and algorithms to automatically generate backgrounds, dramatically improving productivity and reducing time consumption.
3Reliability
If contextual images are used instead of non-contextual images, then visitor engagement increases, but the complexity of selecting and processing appropriate backgrounds increases
Solution Approach 1:
The system employs dynamic background generation that adapts to each specific image and context. Rather than using static pre-defined backgrounds, the saliency map dynamically identifies what needs to be preserved, and the generative model dynamically creates appropriate backgrounds tailored to each case, improving visitor engagement while managing complexity through adaptive processing.
Solution Approach 2:
The patent changes key parameters of the background generation process based on the input image characteristics. The saliency detection adjusts sensitivity parameters, the generative model adjusts creativity and fidelity parameters, and the blending process adjusts mixing ratios, allowing the system to generate engaging backgrounds while adapting to different scenarios without requiring excessive complexity.
Data Source
AI summary
In accordance with the described techniques, a background generation system receives one or more images depicting an object, and textual information describing the object. A generative text model is employed to generate a prompt based on the one or more images and the textual information. Further, a generative image model is employed to generate an output image. To do so, the generative image model generates a background image based on the prompt, and the object is incorporated into the background image. Using a visual saliency model, the background generation system determines a visual saliency defining a degree of fixation on the object within the output image. The background generation system outputs the output image based on the visual saliency meeting a threshold.