Neural Network Palette Generation via Convolution and Upsampling
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Solution Overview
Problem
Current image recognition and segmentation techniques often fail to effectively generate color palettes that evoke desired emotional responses from consumers, as they rely on class labels and pixel-level understanding, which can be costly and inefficient, especially when selecting colors for brands that require specific emotional associations.
Innovation Solution
A neural network system that processes input data, such as images or phrases, to generate palettes by identifying objects, determining colors, calculating overall areas, and associating colors with adjectives, using multi-step convolution and upsampling pathways to create high-dimensional vectors and determine color closeness ratios for palette generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image recognition and segmentation techniques are used to generate color palettes, then class labels and pixel-level understanding can be obtained, but the cost and complexity of color selection increase significantly
Solution Approach 1:
The patent segments the color palette generation process into distinct neural network pathways: a convolution pathway that processes input images to extract features, and an upsampling pathway that reconstructs the palette. This segmentation allows each pathway to specialize in specific tasks, improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary embedding layer that transforms visual features into a latent color space. This embedding layer acts as a mediator between the convolution pathway and the upsampling pathway, enabling the system to generate palettes without requiring direct pixel-level segmentation, thus reducing complexity while maintaining precision.
2Loss of information
If traditional image segmentation techniques are used, then pixel-level understanding of scene elements can be achieved, but the process becomes costly and inefficient for brand color selection
Solution Approach 1:
The patent performs preliminary feature extraction through the convolution pathway before palette generation. By pre-processing the input image to extract relevant visual features and transform them into the latent color space, the system prepares the data in advance, reducing the computational burden during actual palette generation and improving overall efficiency.
Solution Approach 2:
The patent replaces traditional mechanical image segmentation processes with a neural network-based approach. Instead of using complex segmentation algorithms to achieve pixel-level understanding, the system uses learned feature transformations through convolution and embedding layers, substituting mechanical processing with intelligent data transformation that retains color information more efficiently.
3Ease of manufacture
If neural network with multi-step convolution and upsampling pathways is used, then color palettes can be generated efficiently, but the computational complexity of the neural network increases
Solution Approach 1:
The patent transitions from spatial domain processing to a latent color space through the embedding layer. By transforming features into a different dimensional space (the latent color space), the system simplifies the palette generation process, making it easier to implement while the neural network complexity is managed through this dimensional transformation rather than through complex multi-step operations.
Data Source
AI summary
A device to represent an image with a palette is disclosed. The device includes a processor, a memory communicatively coupled to the processor, and a logic. The logic includes a neural network to receive an input data to generate prediction data. The neural network includes a multi-step convolution pathway including a plurality of convolution steps, and a multi-step upsampling pathway including a plurality of upsampling steps. The plurality of upsampling steps includes an input to receive output data from a corresponding convolution pathway step. In response to receiving the input data, feature map output data is generated at the plurality of convolution steps, and at least one step of the plurality of upsampling steps utilizes at least the generated feature map data to generate prediction data. The neural network extracts one or more features from the input data, and generate the prediction data based on the one or more features.