

- Open Access
- Authors : Clark Gnangby Aaron Othniel, Choukpin Adoto Mignonkoun Sourou Yannick
- Paper ID : IJERTV14IS010034
- Volume & Issue : Volume 14, Issue 1 (January 2025)
- Published (First Online): 19-01-2025
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Advancing Image Retrieval Through Similarity Measures using Siamese Neural Networks
Clark Gnangby Aaron Othniel
School of Artificial Intelligence
Nanjing University of Information Science and Technology Nanjing, China
Choukpin Adoto Mignonkoun Sourou Yannick
School of Computer Science
Nanjing University of Information Science and Technology Nanjing, China
AbstractThe rapid growth of digital content has intensified the demand for efficient and accurate image retrieval systems. This research addresses these challenges by harnessing the power of Siamese Neural Networks (SNNs) to enhance similarity measures in image retrieval tasks. SNNs, with their unique architecture designed to learn discriminative features by comparing pairs of images, offer a robust framework for determining image similarity. By training the network to minimize the distance between similar images and maximize it between dissimilar ones, we can achieve highly accurate retrieval results. This study explores various similarity metrics and their integration within the Siamese network framework to optimize retrieval performance. Through rigorous experimentation on the AT&T Face Dataset, our SNN model achieved an impressive accuracy of 81% in face recognition tasks. These results underscore the effectiveness of our approach in improving retrieval accuracy and efficiency, highlighting its potential application in large-scale image databases. Our findings contribute to the advancement of image retrieval technologies, providing a sophisticated solution to the challenges posed by contemporary digital image repositories.
KeywordsImage Retrieval; Siamese Neural Networks; Similarity Measures; Deep Learning; Feature Extraction; Pairwise Learning; Face Recognition; Pattern Matching; Neural Network Architecture.
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INTRODUCTION
Image retrieval, a discipline within computer vision and artificial intelligence, focuses on the search and extraction of images from a database based on specific queries. This field is primarily divided into two approaches: Content-Based Image Retrieval (CBIR) and Metadata-Based Image Retrieval. CBIR involves searching for images based on their visual content, such as colors, shapes, and textures, utilizing algorithms to compare and match query images with those in the database. In contrast, Metadata-Based Image Retrieval relies on associated textual information, such as tags and descriptions, to index and retrieve images. This field finds applications across various domains, notably in image search engines like Google Images, enabling users to search for images using keywords, and recommendation systems such as Pinterest, which suggest similar images based on user preferences. Additionally, image retrieval is essential for plagiarism detection by comparing images to identify potential copies, as well as in object recognition for surveillance and security purposes. Several techniques drive image retrieval systems forward. Feature descriptors such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Histogram of
Oriented Gradients (HOG) are employed to extract visual features from images. Furthermore, Convolutional Neural Networks (CNNs) enable the extraction of more complex and abstract features. Efficient indexing and retrieval techniques, such as search trees and hashing, are also utilized to accelerate the search process, particularly in large image databases. Continuously evolving, image retrieval remains at the forefront of technological advancement, constantly enhancing the accuracy and efficiency of image search systems.
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PROBLEM STATEMENT
Image retrieval, or the search for images, is a complex task with several significant challenges. Extracting an appropriate representation of visual features that captures relevant semantic aspects such as objects, scenes, textures, etc., is a key challenge. Traditional approaches based on low-level features, such as color histograms, are often non-discriminative and insufficient for capturing the semantic nuances of images. On the other hand, learning high-level representations using convolutional neural networks (CNNs) requires large amounts of annotated data, which can be costly and difficult to obtain. Enabling efficient search and matching in very large image databases, potentially containing billions of images, while maintaining reasonable response times is a major technical challenge. Algorithms need to be both fast and accurate to handle vast amounts of visual data. Integrating user feedback on the relevance of results to refine queries and improve performance over time is an active area of research. Systems must be able to learn and adapt based on user preferences and needs to provide increasingly relevant results. Effectively combining visual information with other modalities such as text, metadata, or context is essential for improving the retrieval of relevant images. This allows leveraging different sources of information to enrich image search. Defining robust benchmarks and evaluation metrics that accurately reflect the performance perceived by human users remains an open challenge. Systems must be evaluated in a way that ensures they meet user expectations and needs in various contexts.
Among these challenges, the similarity measure stands out as a fundamental problem in image retrieval. Defining an appropriate similarity measure to compare the query to the images in the database is crucial. Classical measures, such as Euclidean distance, can be suboptimal for capturing the semantic similarity perceived by humans. An effective similarity measure must reflect human perception and allow
precise distinction between relevant and irrelevant images. This problem is particularly complex as it requires a deep understanding of the semantic and contextual aspects of images. In this context, our research focuses on this specific problem of similarity measure in image retrieval. We explore the use of Siamese Neural Networks to develop more accurate and efficient methods for image comparison. By improving similarity measures, we aim to align search results with human perception and optimize the performance of image retrieval systems. This approach promises to overcome some limitations of traditional methods and offer significant advancements in the field of image retrieval.
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RELATED WORKS
Koch et al. introduced a Siamese neural network architecture for one-shot image recognition, which is highly relevant to image retrieval tasks. Their approach leveraged a contrastive loss function, showing that the model could achieve 97% accuracy on the Omniglot dataset. This study demonstrated the potential of Siamese networks in efficiently learning similarity measures with minimal data, significantly improving the retrieval of relevant images [1].
Hoffer and Ailon proposed the Triplet Network for image similarity learning in large-scale retrieval tasks. They employed triplet loss to optimize the relative distances between anchor, positive, and negative image pairs. Their method outperformed previous models, achieving 83.2% accuracy on the CUB-200-2011 dataset. This work emphasized the effectiveness of triplet-based learning in capturing relative similarities between images in a high-dimensional space [2].
Dai et al. applied Siamese networks with contrastive loss for image similarity learning. They evaluated their method on the CIFAR-10 dataset and observed an 8% improvement in Mean Average Precision (MAP) compared to traditional k-NN approaches. Their results reinforced he power of Siamese networks to enhance image retrieval performance by refining similarity measures in large image databases [3].
Shen et al. further explored the use of deep metric learning with triplet loss for image retrieval. Their approach achieved a MAP of 84.1% on the Stanford Online Products dataset, significantly improving retrieval accuracy by better clustering similar images while distinguishing dissimilar ones. Their work demonstrated the value of triplet loss in refining the precision of similarity-based retrieval [4].
Lim et al. extended the use of Siamese networks by combining them with deep metric learning for improved image retrieval. Their study, which tested on various datasets like CIFAR-10 and ImageNet, showed a 30% increase in retrieval accuracy compared to traditional methods. This work highlighted the effectiveness of deep metric learning in capturing high-level semantic similarities for image retrieval [5].
Gilakjani and Al Osman explored the use of Graph Neural Networks (GNNs) combined with contrastive learning for emotion recognition based on EEG signals, drawing parallels to image retrieval. By applying contrastive learning to create discriminative features, their approach improved classification accuracy. Their research suggests that similar techniques could enhance image retrieval systems by better understanding the underlying relationships between images and their features [6].
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METHODOLOGY
To solve the problem of defining an appropriate similarity measure in image retrieval, several approaches can be taken, particularly leveraging advanced machine learning techniques such as deep learning and specifically, Siamese Neural Networks. Consider a scenario where an attendance system is needed for a small organization with just 20 employees (keeping the number small for simplicity). The system needs to be capable of recognizing each employee's face [7].
Fig. 1 An attendance system
The initial challenge will be acquiring training data images, as we need numerous varied images of each employee in the organization. When a new employee joins or an existing one leaves, we have to gather new data and retrain the entire model. This approach is inefficient for a scalable system, particularly for large organizations like multinational corporations (MNCs), where employee turnover occurs frequently.
In such cases, a Siamese network model can be a great solution for a scalable system.
Instead of classifying a test image as one of the 20 employees, the Siamese network takes a reference image of the person and generates a similarity score indicating the likelihood that the two input images are of the same person.
Fig. 2 Siamese Neural Network Principle
The similarity score ranges from 0 to 1, using a sigmoid function. A score of 0 indicates no similarity, while a score of 1 indicates full similarity. Scores between 0 and 1 represent varying degrees of similarity.
Siamese networks do not learn to classify an image into predefined output classes. Instead, they learn using a similarity function, which takes two images as input and provides the probability of how similar these images are.
Unlike traditional neural networks in deep learning, a Siamese network does not require a large number of instances for each class; a few instances are sufficient to build a good model.
The biggest advantage of the Siamese network is that, for face detection applications like attendance systems, adding a new employee or class is simple. The model only needs a single image of the new employee's face. Using this single image as the reference, the network can calculate the similarity score for any new instances presented to it. This ability exemplifies the network's one-shot learning capability, as it can make predictions based on just one example.
Here's a step-by-step explanation of how a Siamese network architecture works:
Input Images: We start with two images that we want to compare to determine if they are similar or dissimilar pairs.
First Subnetwork:
The first image (A) is input into the first subnetwork.
This image passes through several convolutional layers and fully connected layers.
The output is a vector representation of the image, called an encoding E(A).
Second Subnetwork:
The second image (B) is input into a second subnetwork that is identical to the first, sharing the same weights and parameters.
This image also passes through convolutional layers and fully connected layers.
The output is another vector representation of the image, called an encoding E(B).
Comparing Encodings:
We now have two encodings, E(A) and E(B), from the respective images.
These encodings are compared to determine how similar the two images are.
Fig. 3 Our Siamese Neural Network Architecture
Distance Measurement:
The distance between the two vectors E(A) and E(B) is calculated.
If the distance is small, it indicates that the vectors (and thus the images) are similar or belong to the same class.
If the distance is large, it indicates that the vectors (and thus the images) are different from one another.
The similarity score based on this distance determines how similar or dissimilar the images are. This process allows the Siamese network to effectively compare images and perform tasks like face recognition with high accuracy.
In Siamese network architecture, loss functions are crucial for distinguishing similar and dissimilar pairs of images.
Loss Functions in Siamese Networks
There are two primary loss functions used in Siamese networks: contrastive loss and triplet loss.
Contrastive Loss Function
Siamese networks are designed to differentiate between input images rather than classify them. Therefore, traditional classification loss functions like cross-entropy loss are not suitable. Instead, we use the contrastive loss function.
Triplet Loss Function
Another effective loss function for Siamese networks is the triplet loss, which is particularly useful for tasks requiring fine- grained similarity assessments.
Instead, the Siamese network architecture is better suited to use a contrastive loss function.
This function evaluates how effectively the Siamese network distinguishes between given image pairs.
The formula for the contrastive loss function is as follows:
Where Dw is defined as the Euclidean distance between the outputs of the sister networks.
The mathematical formula for the Euclidean distance is:
Y is either 1 or 0. If the first image and the second image are from the same class, then Y is 0; otherwise, Y is 1.
max () is a function that returns the higher value between 0 and m – Dw.
m is a margin value greater than 0. This margin ensures that dissimilar pairs beyond this threshold do not contribute to the loss.
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RESULTS AND EXPERIMENTAL ANALYSIS
In this section, we analyze the performance of the Siamese Neural Network (SNN) trained for face verification tasks using the AT&T face dataset [8]. The AT&T Face Dataset, also known as the ORL Database of Faces, is a publicly available dataset commonly used for research in face recognition,
machine learning, and computer vision. It consists of 400 grayscale images of 40 distinct individuals, with each individual having 10 images taken at different times. These images capture various facial expressions, such as smiling and not smiling, and different facial details, including wearing glasses or not. The images also vary in lighting, pose (including slight left or right tilt, up or down), and facial details, providing a diverse set for testing and training. Each image has a resolution of 92×112 pixels and features a plain dark background, typically stored in the PGM format, a standard grayscale image format. The training and evaluation of the network were conducted over 100 epochs, and the results of the training process, as well as the performance metrics on the test dataset, are discussed below.
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Training Loss
During the training phase, the network was optimized using the Contrastive Loss function. The training loss was recorded at regular intervals, showing the following progression over 100 epochs:
Table 1 : Training Loss over time
Epoch
Training Loss
Epoch 0
1.996
Epoch 1
1.0104
Epoch 2
0.7852
***
***
Epoch 100
0.0224
From the plot, we observe a significant decrease in the training loss over the epochs, indicating that the model is learning to differentiate between similar and dissimilar pairs effectively. Initially, the loss drops sharply, showing rapid learning, and then it gradually decreases, converging to a lower value, suggesting stabilization.
Fig. 4 Training Loss over the epochs
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Performance Metrics on Test Dataset
The performance of the SNN was evaluated using the test dataset. The following metrics were computed to quantify the model's performance.
Table 2 : Our Model's performance
Metrics
Performances
Accuracy
0.8100
Precision
0.8000
Recall
0.9375
F1-Score
0.7317
These metrics are essential in evaluating how effectively the model identifies whether two images belong to the same class (i.e., represent the same person) or not.
Accuracy:
Accuracy is the overall measure of the model's correctness. It calculates the proportion of correct predictions (both true positives and true negatives) out of all predictions.
Precision:
Precision is the proportion of true positives (correctly identified pairs) among all positive predictions made by the model (i.e., all predicted pairs that were classified as similar). A high precision indicates fewer false positives but does not account for false negatives.
Recall (Sensitivity):
Recall, also known as sensitivity or true positive rate, measures the proportion of true positives among all actual positives (i.e., all pairs that actually belong to the same class). High recall means the model is good at identifying true positives, but it does not account for false positives.
F1 Score:
The F1 Score is the harmonic mean of precision and recall. It provides a balanced view of the models performance, especially when dealing with imbalanced datasets.
The F1 score combines both precision and recall into one metric, offering a trade-off between them.
In our case, the model has a relatively high recall of 0.9375, suggesting that it is very effective at identifying similar pairs (true positives). However, the precision of 0.810 indicates that there is a moderate rate of false positives, meaning the model is also identifying some dissimilar pairs as similar. The F1 score of 0.7317 provides a balanced view of the model's performance, indicating that it strikes a reasonable trade-off between precision and recall Table 2.
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Visual Analysis
To further analyze the model's performance, we visualized the dissimilarity scores for pairs of test images. Below are a few examples:
including extracting high-level visual features, scaling to large databases, integrating user feedback, and evaluating system performance effectively.
By leveraging SNNs, we created a robust embedding space where the similarity between images could be accurately measured using Euclidean distance or cosine similarity. This method offers significant improvements over traditional similarity measures, which often fail to capture the nuanced semantic relationships between images. Key steps in our approach included creating pairs of images labeled as similar or dissimilar to train the SNN, building twin networks that share weights and are designed to extract meaningful features from images, and using contrastive loss to ensure that similar images are close in the embedding space and dissimilar images are far apart. To handle large-scale data, we implemented techniques such as Approximate Nearest Neighbor (ANN) search and distributed computing. Collecting and incorporating user feedback allowed us to continually refine the similarity measure, while benchmark datasets and robust evaluation metrics were used to assess system performance.
The implementation of SNNs in this context demonstrated that it is possible to significantly enhance the precision and accuracy of image retrieval systems. By harmonizing the similarity measure with human perception, our approach offers a promising solution to the inherent complexities of image retrieval, ultimately improving user experience. Future work can expand on this foundation by exploring advanced fusion techniques to combine visual features with other modalities, further refining the embedding space, and continuously integrating user feedback to adapt to evolving search needs. Overall, the use of Siamese Neural Networks represents a powerful and effective strategy for advancing the field of image retrieval.
Fig. 5 Dissimilarity scores for pairs of test images
The dissimilarity scores range from low values (indicating high similarity) to higher values (indicating dissimilarity). These visual results help in qualitatively assessing the model's decision-making process.
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CONCLUSION
In experimental report, we tackled the fundamental problem of defining an appropriate similarity measure for image retrieval using Siamese Neural Networks (SNNs). Our approach was designed to address several key challenges in image retrieval,
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DISCUSSION
The use of Siamese Neural Networks (SNNs) for image retrieval represents a significant advancement in addressing the complex problem of measuring image similarity. This discussion explores the strengths, limitations, and future directions of our approach, highlighting the broader implications for the field of computer vision and image retrieval.
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Strengths of the Approach
One of the primary strengths of using SNNs is their ability to learn high-level, semantically rich representations of images. Unlike traditional methods that rely on low-level features such as color histograms, SNNs can capture complex patterns and relationships within images, resulting in more accurate and human-like similarity assessments. The contrastive loss function used during training ensures that similar images are mapped close to each other in the embedding space, while dissimilar images are placed far apart. This facilitates more precise image retrieval, aligning results with human perception. Another significant advantage is scalability. Techniques such as Approximate Nearest Neighbor (ANN) search and distributed computing frameworks enable the handling of large-scale databases, ensuring that our system remains efficient even with billions of images. This is crucial for real- world applications where the volume of data can be vast.
The integration of user feedback into the system provides a dynamic way to continually improve the retrieval process. By incorporating relevance feedback, the system can adapt to user preferences over time, enhancing the relevance of search results and improving user satisfaction.
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Limitations and Challenges
Despite these strengths, several limitations and challenges remain. Training SNNs requires a substantial amount of labeled data, which can be costly and time-consuming to otain. While data augmentation and semi-supervised learning can mitigate this to some extent, the reliance on annotated data is a significant hurdle.
Additionally, the embedding space created by SNNs, while powerful, is not infallible. There can be instances where the learned representations fail to capture subtle semantic differences, leading to less accurate retrieval results. Further refinement of the network architecture and loss functions might be necessary to address these issues.
Scalability, while largely addressed by ANN and distributed computing, still poses challenges. Maintaining performance in terms of speed and accuracy as the database grows requires continuous optimization and potentially novel indexing techniques.
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Future Directions
Future research can explore several promising directions to enhance this approach. One area is the fusion of multimodal information. Combining visual features with textual metadata, contextual information, and other modalities can provide a richer, more comprehensive understanding of images, leading to even more accurate retrieval results.
Advancements in unsupervised and self-supervised learning techniques could reduce the dependency on labeled data, making the training process more efficient and less costly. These techniques can leverage large amounts of unlabeled data to learn useful representations, which can then be fine-tuned with smaller labeled datasets.
Another potential improvement is the integration of more sophisticated user feedback mechanisms, such as interactive learning, where users can iteratively refine their queries and provide detailed feedback on search results. This could lead to a more personalized and adaptive image retrieval system.
Lastly, continuous evaluation and refinement of performance metrics are essential. Developing new benchmarks and evaluation methods that better capture the user experience and the semantic quality of retrieval results will provide a clearer picture of system performance and guide further improvements.
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Broader Implications
The advancements in image retrieval using SNNs have broader implications for various applications, including e-commerce, digital asset management, medical imaging, and more. Improved image retrieval systems can enhance user experiences, streamline workflows, and provide more relevant results across these domains.
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