Electronic Picture Frame Environmental Adaptation via Image Recognition
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Solution Overview
Problem
Electronic picture frames do not consider their environment when recommending artworks, resulting in mismatched recommendations.
Innovation Solution
A display device equipped with a processor that uses a deep learning-based image recognition model to categorize the environment and a decision tree model to determine matching pictures from a library, prioritizing decoration style, tone, and furniture type, allowing for adaptive artwork recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the electronic picture frame recommends artworks based on user browse history only, then the recommendation system is simple to implement, but the recommendation accuracy and environmental adaptability deteriorate
Solution Approach 1:
The patent segments the environment recognition task into multiple independent modules: image acquisition module, category recognition module (using deep learning models), and artwork matching module. This segmentation allows each module to be optimized independently while maintaining overall system adaptability without excessive complexity.
Solution Approach 2:
The system performs preliminary environmental analysis by capturing and categorizing images of the installation environment before artwork selection. The deep learning model pre-processes environmental features (decoration style, color scheme, furniture type) to prepare matching criteria, enabling adaptive recommendations without real-time complex computations during artwork selection.
2Measurement precision
If deep learning-based image recognition is used to categorize the environment, then the environmental recognition accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by using pre-trained deep learning models that have already learned general environmental features. Instead of training comprehensive models from scratch, the system uses transfer learning with models pre-trained on large datasets, achieving high recognition accuracy with reduced computational energy during deployment.
Solution Approach 2:
Environmental images are captured and processed in advance to establish category labels (decoration style, color tone, furniture type). These pre-processed environmental features are stored and reused for multiple artwork matching operations, avoiding repeated heavy computational processing for the same environment.
3Manufacturing precision
If multiple environmental categories (decoration style, tone, furniture type) are considered for artwork matching, then the aesthetic alignment and user satisfaction improve, but the complexity of the matching algorithm increases
Solution Approach 1:
The patent segments the artwork matching process into independent evaluation dimensions: decoration style matching, color tone matching, and furniture type matching. Each dimension is evaluated separately using specific criteria, and the results are combined to determine overall compatibility. This segmentation simplifies the algorithm by breaking down the complex multi-dimensional matching problem into manageable independent tasks.
Solution Approach 2:
The system applies different matching criteria and weightings to different environmental categories based on their local importance. For example, decoration style may be given higher weight in certain contexts while color tone becomes more important in others. This local quality approach allows precise matching in each dimension while keeping the overall algorithm flexible and manageable.
Data Source
AI summary
The present disclosure relates to a display device and its display device, an electronic picture frame and a computer readable storage medium. The display device includes: a processor configured to acquire an environmental image of the environment where the display device is located, identify a category of the environmental image, and determine one or more pictures matching the category from a picture library; and a display configured to display at least one of the determined pictures.