Object Re-Recognition Model Using Multi-Feature Attention
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Solution Overview
Problem
Existing image re-recognition methods based on hand-crafted features lack generalization and are time-consuming, while deep learning methods fail to capture subtle differences and ignore local information, resulting in low accuracy.
Innovation Solution
A method for re-recognizing object images using multi-feature information capture and correlation analysis, which involves establishing a model with a cross-entropy loss function and triplet loss function, incorporating convolutional layers with spatial and channel attention mechanisms, and a transformer encoder with multi-head attention for improved feature representation and retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-crafted feature methods are used for image re-recognition, then the implementation is straightforward, but the generalization capability is poor and the processing time is excessive
Solution Approach 1:
The patent replaces hand-crafted feature extraction with a deep learning-based automatic feature extraction system. The convolutional neural network automatically learns and extracts features from images, eliminating the need for manual feature engineering while significantly improving processing speed and generalization capability.
2Productivity
If deep learning methods focusing on global information are used, then the processing speed is improved, but the ability to capture subtle differences and local information is lost, resulting in low accuracy
Solution Approach 1:
The patent divides the image into multiple local regions and processes each region separately through the neural network. This segmentation allows the model to capture both global context and local details, improving the ability to distinguish subtle differences between similar objects while maintaining processing efficiency.
Solution Approach 2:
The patent applies different processing weights to different regions of the image, emphasizing important local features while maintaining global context. This allows the model to focus computational resources on critical areas, improving recognition accuracy without sacrificing processing speed.
3Productivity
If deep learning methods capturing partial information are used, then the processing is faster, but the overall information is not considered, resulting in low re-recognition accuracy
Solution Approach 1:
The patent combines multiple feature representations and information sources within the neural network architecture. By merging local and global features, and integrating information from different processing stages, the model achieves comprehensive understanding of the image while maintaining efficient processing.
Solution Approach 2:
The patent ensures continuous flow of information through the neural network architecture, where features are progressively refined and combined at each layer. This continuous processing allows the model to maintain both speed and accuracy by constantly integrating new information without interrupting the processing pipeline.
Data Source
AI summary
A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.