Visual Attention Center Prediction for Progressive Loading
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Solution Overview
Problem
Existing saliency maps fail to capture temporal aspects of human visual attention, such as the initial focus point and image-level attention dynamics, limiting their effectiveness in applications like progressive image loading and image editing.
Innovation Solution
A machine-learned visual attention center prediction model that identifies a single center of human visual attention over a period of viewing time, trained using supervised learning with temporal and spatial filtering of attention points, enabling efficient prediction of the visual attention center for images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If saliency maps are used to identify important image regions, then the importance of regions can be represented, but temporal aspects of attention such as initial focus point and attention dynamics are not captured
Solution Approach 1:
The patent extracts only the essential temporal information (initial focus point and attention center) from the complex saliency map data, discarding redundant temporal details while preserving the critical attention center location. This extraction approach resolves the contradiction by losing minimal information while significantly reducing computational complexity.
Solution Approach 2:
The model performs preliminary processing of attention data to identify the attention center before it is needed for image processing tasks. By pre-computing the attention center location, the system avoids complex real-time temporal analysis while ensuring accurate representation of human visual attention characteristics.
2Measurement precision
If saliency maps are used to identify regions viewed longest, then attention duration can be measured, but the center of visual attention for the image as a whole cannot be identified
Solution Approach 1:
The patent merges multiple attention measurement approaches (initial focus point detection and attention center calculation) into a unified model that simultaneously provides both temporal and spatial attention information. This consolidation improves measurement precision while maintaining processing efficiency by avoiding separate analysis steps.
Solution Approach 2:
The visual attention center prediction model serves multiple functions: it identifies the attention center location, determines initial focus point, and provides image-level attention dynamics information. This multi-functionality resolves the contradiction by achieving precise measurement through a single efficient model rather than multiple specialized systems.
3Loss of information
If comprehensive attention analysis is performed to capture all attention dynamics, then complete attention information can be obtained, but computational resources and memory are consumed excessively
Solution Approach 1:
The model extracts only the essential attention center information from comprehensive attention dynamics data, filtering out redundant temporal and spatial details. This extraction significantly reduces computational energy consumption while preserving the critical information needed for image processing applications.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of attention data (the attention center region) rather than analyzing the entire image and all temporal frames. This partial processing approach reduces computational energy consumption while maintaining sufficient accuracy for practical applications.
Data Source
AI summary
Provided are systems and methods for training and using a machine-learned model to predict a visual attention center for an image. As one example, the predicted visual attention center for the image can be used in ordering image regions for encoding, decoding, transmitting, and/or loading in a progressive image loading format.