Visual Attention Model Feature Impact Assessment
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Solution Overview
Problem
Content creators face challenges in understanding and manipulating visual attention in scenes due to the complexity of visual attention models, which rely on various features without providing clear insights into how to alter attention allocation effectively.
Innovation Solution
A method and system that apply visual attention models to assess the impact of specific visual features within a scene, allowing users to compute and present feature-related data to inform changes that can alter visual attention, using a computer-based system with input, processing, and output modules to determine feature contributions to visual conspicuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual attention models are applied to determine visual conspicuity, then measurement precision of attention allocation is improved, but device complexity increases due to the complexity of visual attention models
Solution Approach 1:
The system segments the complex visual attention model into distinct functional modules: an input module for receiving visual representations, a VAM module for applying the attention model and computing feature-related data, and an output module for presenting results. This modular segmentation maintains measurement precision while making the complex system more manageable and interpretable.
Solution Approach 2:
The patent introduces an intermediary processing layer that computes feature-related data (such as saliency maps, feature contributions, and attention scores) that bridges the complex internal computations of the visual attention model and the user-interpretable output. This intermediary layer translates complex model operations into meaningful metrics without sacrificing measurement precision.
2Loss of information
If feature-related data is computed to assess impact of visual features, then loss of information is reduced by providing insights into attention allocation, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system implements feedback by computing and presenting feature-related data that provides insights into how different visual features contribute to attention allocation. The output module presents information about which features (e.g., color, texture, shape) most influence visual conspicuity, enabling users to understand and adjust content to achieve desired attention patterns without losing critical information about attention mechanisms.
3Measurement precision
If visual attention modeling is applied to understand attention allocation, then measurement precision is improved, but ease of operation deteriorates due to complexity of interpreting and manipulating visual features
Solution Approach 1:
The patent employs visual representations of feature-related data where different visual features are represented through distinct visual channels (such as color coding, intensity variations, or spatial highlighting). This allows users to easily distinguish and understand the contribution of different features to attention allocation, improving ease of operation while maintaining measurement precision through the underlying computational model.
Data Source
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AI summary
At least some embodiments of the present disclosure feature systems and methods for assessing the impact of visual features within a region of a scene. With the input of a visual representation of a scene and at least one selected region within the scene, the system applies a visual attention model to the visual representation to determine visual conspicuity of the at least one selected region. The system computes feature-related data associated with a plurality of features of the at least one selected region. Based on the visual conspicuity and the feature-related data, the system assesses an impact that at least one of the features within the at least one selected region have on the visual conspicuity.