Multi-task Deep Hash Learning for Logistics Image Retrieval
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Solution Overview
Problem
Existing methods for image retrieval in massive logistics product databases face inefficiencies due to high dimensionality and large data sizes, leading to limitations in nearest neighbor search, and require retraining when Hash code lengths change, resulting in resource waste and reduced retrieval accuracy.
Innovation Solution
A multi-task deep Hash learning-based retrieval method that constructs a similarity matrix and uses a multi-branch network to learn Hash codes of various lengths, optimizing similarity and mutual information loss functions to improve representational capacity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Hash codes of different lengths are learned separately through model retraining, then retrieval accuracy for specific code lengths can be optimized, but hardware resources are wasted and time cost increases
Solution Approach 1:
The patent merges multiple Hash code learning tasks of different lengths into a single multi-task deep Hash learning model. The network simultaneously learns Hash codes of multiple lengths (e.g., 16-bit, 32-bit, 48-bit, 64-bit) through shared low-level feature extraction layers, eliminating the need for separate model training for each code length while maintaining retrieval accuracy.
Solution Approach 2:
The invention creates a universal Hash learning model that can generate Hash codes of various lengths for different retrieval scenarios. The multi-branch network structure allows the same model to serve multiple functions by producing Hash codes of different lengths based on the specific retrieval requirements, reducing the need for multiple specialized models.
2Measurement precision
If Hash codes of different lengths are learned separately through model retraining, then retrieval accuracy for specific code lengths can be optimized, but hardware resources are wasted
Solution Approach 1:
The patent merges multiple Hash code learning tasks of different lengths into a single multi-task deep Hash learning model. The network simultaneously learns Hash codes of multiple lengths (e.g., 16-bit, 32-bit, 48-bit, 64-bit) through shared low-level feature extraction layers, eliminating the need for separate model training for each code length while maintaining retrieval accuracy.
Solution Approach 2:
The invention creates a universal Hash learning model that can generate Hash codes of various lengths for different retrieval scenarios. The multi-branch network structure allows the same model to serve multiple functions by producing Hash codes of different lengths based on the specific retrieval requirements, reducing the need for multiple specialized models.
3Device complexity
If only Hash codes of a single length are considered, then the model structure is simpler, but the potential relationship between different length codes is ignored resulting in reduced representational capacity
Solution Approach 1:
The patent segments the Hash learning process into multiple branches, each responsible for learning Hash codes of a specific length. The low-level feature extraction is shared across all branches, while high-level features are branch-specific, allowing the model to capture both common and length-specific characteristics of Hash codes.
Solution Approach 2:
The invention extends the Hash code representation by considering multiple dimensions (different lengths) simultaneously. The multi-branch network processes features at different dimensionalities, capturing relationships between Hash codes of various lengths and preserving interactive information that would be lost in single-length approaches.
4Ease of manufacture
If linear non-depth Hash algorithms are used, then the implementation is simpler, but feature extraction and Hash function learning are asynchronous making optimization difficult
Solution Approach 1:
The patent replaces traditional linear non-depth Hash algorithms with a deep neural network-based approach. The deep Hash learning model uses differentiable operations throughout the network, allowing gradient-based optimization to simultaneously learn features and Hash functions in an end-to-end manner, overcoming the asynchronous optimization limitation of linear methods.
Data Source
AI summary
The present disclosure provides a multi-task deep Hash learning-based retrieval method for massive logistics product images. According to the idea of multi-tasking, Hash codes of a plurality of lengths can be learned simultaneously as high-level image representation. Compared with single-tasking in the prior art, the method overcomes shortcomings such as waste of hardware resources and high time cost caused by model retraining under single-tasking. Compared with the traditional idea of learning a single Hash code as an image representation and using it for retrieval, information association among Hash codes of a plurality of lengths is mined, and the mutual information loss is designed to improve the representational capacity of the Hash codes, which addresses the poor representational capacity of a single Hash code, and thus improves the retrieval performance of Hash codes.