Cross-Modal Retrieval via Binary Hashing and Quantization
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Solution Overview
Problem
Existing cross-modal retrieval methods face inefficiencies in memory and computational resources due to limitations in dense feature vector searches, while binary code-based methods are less accurate and quantization-code-based methods are computationally inefficient.
Innovation Solution
A system that simultaneously learns neural network models, binary hash algorithms, and quantization algorithms to generate feature vectors, binary hash codes, and quantization codes across multiple data modalities, using a loss function that preserves semantic similarity correlations, thereby narrowing the search space and improving retrieval efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense feature vectors are used for cross-modal retrieval, then retrieval accuracy is improved, but memory and computational efficiency deteriorate
Solution Approach 1:
The patent segments the feature representation into two parts: a binary hash code for coarse retrieval and quantization codes for fine-grained retrieval. This segmentation allows the system to use computational resources efficiently by applying different representation methods to different stages of the retrieval process, resolving the contradiction between accuracy and computational efficiency.
Solution Approach 2:
The patent transforms continuous feature vectors into binary hash codes (discrete dimension) and quantization codes (low-dimensional continuous approximation). This dimensional transformation reduces the computational complexity while preserving semantic information, enabling efficient cross-modal retrieval without sacrificing accuracy.
2Use of energy by moving object
If binary codes are used for cross-modal retrieval, then computational efficiency is improved, but retrieval accuracy deteriorates
Solution Approach 1:
The patent merges binary hash codes and quantization codes into a unified retrieval framework. The binary codes provide efficient coarse filtering while quantization codes provide accurate fine-grained matching. By combining both representations, the system achieves both computational efficiency and high retrieval accuracy simultaneously.
Solution Approach 2:
The patent uses binary hash codes for preliminary retrieval filtering before applying quantization codes for final ranking. This preliminary action narrows down the search space efficiently, and the subsequent fine-grained retrieval using quantization codes ensures accuracy, resolving the trade-off between speed and precision.
3Measurement precision
If quantization codes are used for cross-modal retrieval, then retrieval accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the retrieval process into two stages: coarse retrieval using binary hash codes and fine-grained retrieval using quantization codes. This segmentation allows the system to avoid computationally expensive quantization operations on the entire dataset, applying them only to a reduced set of candidates, thus maintaining accuracy while improving overall efficiency.
Solution Approach 2:
The patent applies quantization codes partially - only to the subset of data items that pass the binary hash filtering threshold. This partial application of the more computationally intensive quantization operation reduces the overall computational burden while maintaining the accuracy benefits of quantization for the relevant subset of data.
Data Source
AI summary
The present disclosure relates to a new method for cross-modal retrieval via deep binary hashing and quantization. In a training phase, the system simultaneously learns to generate feature vectors, binary codes, and quantization codes for data across two or more modalities that preserves the semantic similarity correlations in the original data. In a prediction phase, the system retrieve a data item in a database that is semantically similar to a query item of a different modality. To identify the database item closest in semantic meaning to the query item, the system first narrows the database search space based on binary hash code distances between each of the database items and the query item. The system then measures the quantization distances between the query items and the database items in the smaller search space. The system identifies database item have the closest quantization distance to the query item as the closest semantic match to the query item.