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# A Survey of Hash Techniques for Image Retrieval:
## Advancements, Challenges, and Applications

Abdelilah Ganmati, Karim Afdel, Lahcen Koutti

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Abdelilah Ganmati, Karim Afdel, Lahcen Koutti. A Survey of Hash Techniques for Image Retrieval: Advancements, Challenges, and Applications. 2025. hal-05283675

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# A Survey of Hash Techniques for Image Retrieval: Advancements, Challenges, and Applications

Abdelilah Ganmati*

Karim Afdel†

Lahcen Koutti‡

## Abstract

The rapid growth of digital image data has necessitated efficient techniques for content-based image retrieval. Hashing methods, which transform high-dimensional data into compact binary codes, have emerged as powerful tools to address this challenge. This survey presents a comprehensive overview of state-of-the-art hashing techniques for image retrieval. We delve into a wide range of methodologies, discussing their respective advancements, challenges, and potential applications. Our survey covers methods that utilise deep learning, supervised and unsupervised strategies, and various loss functions to generate binary hash codes. We explore the context and motivations behind each technique, providing insights into their effectiveness and limitations. By shedding light on the strengths and weaknesses of different hashing approaches, this survey aims to provide a valuable resource for researchers and practitioners seeking to understand and leverage the advancements in image retrieval using hash techniques.

**Keywords** Image retrieval, hashing techniques, binary codes, deep learning, supervised hashing, unsupervised hashing, similarity preservation, content-based retrieval, survey.

## 1 Introduction

Image retrieval, a fundamental task in the field of computer vision, plays a pivotal role in various applications ranging from content-based image search to multimedia management. The exponential growth in digital imagery has driven the need for efficient and accurate methods to retrieve relevant images from large-scale databases. Among the diverse approaches, hashing-based techniques have gained significant attention for their ability to expedite the retrieval process by mapping high-dimensional data to compact binary codes. These binary codes enable fast and memory-efficient nearest neighbour searches, thereby addressing the computational and storage challenges posed by the massive volumes of visual data.

The evolution of hashing techniques has witnessed a transformative shift with the integration of deep learning methodologies. Deep hashing, a fusion of deep neural networks and binary coding, has emerged as a powerful paradigm to extract and encode the inherent semantic information or features of images. This amalgamation capitalises on the representational capabilities of convolutional neural network (CNN) architectures to enable enhanced image retrieval performance. Consequently, the landscape of image retrieval has been reshaped, opening up new avenues for precision and efficiency.

<footnote>*Computer Systems & Vision Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco. Email: a.ganmati@uiz.ac.ma. ORCID: 0009-0006-9450-9239.</footnote>

<footnote>†Computer Systems & Vision Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco. Email: k.afdel@uiz.ac.ma. ORCID: 0000-0002-0828-2116.</footnote>

<footnote>‡Computer Systems & Vision Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco. Email: l.koutti@uiz.ac.ma. ORCID: 0000-0002-4274-3414.</footnote>
While several survey articles have explored various aspects of deep supervised hashing techniques for image retrieval, each publication presents a distinctive perspective, insights, and synthesis of research. In this survey, we embark on a comprehensive exploration of the field, delving into the intricacies of deep supervised hashing methods and their applications. Our aim is to dissect the existing approaches, scrutinise their strengths and limitations, and identify the directions in which the field is progressing. By providing an up-to-date analysis and classification of these methods, we intend to offer a valuable resource for researchers, practitioners, and enthusiasts seeking to navigate the evolving landscape of deep supervised hashing in image retrieval.

This survey is organized as follows. In Section 2, we briefly introduce the foundational concepts of hashing and its significance in image retrieval. Section 3 provides a thorough examination of existing deep supervised hashing methods, elucidating the diverse strategies employed to leverage deep neural networks for enhancing the retrieval process. We categorize the methods based on their input treatment, enabling readers to comprehend the distinct perspectives they offer. Our exploration culminates in Section 4, where we present representative applications, and Section 5 discusses open challenges and future directions.

# 2 Hashing in Image Retrieval: Fundamentals and Significance

Hashing, a technique rooted in computer science and information retrieval, has emerged as a potent tool for efficient data retrieval, especially in the context of image retrieval. This section elucidates the foundational concepts of hashing and expounds on its significance in addressing the challenges posed by large-scale image databases.

## 2.1 Overview of Hashing Techniques

Hashing, at its core, involves the transformation of input data into a fixed-size hash code, typically represented as a binary string. This compact representation allows for expedited retrieval operations by minimizing the search space. Hashing techniques can be classified into two categories: traditional and data-dependent. Traditional hashing algorithms, such as MD5 and SHA-1, map data to hash codes without considering the data distribution. In contrast, data-dependent methods learn hash functions that exploit data characteristics, enhancing retrieval performance by capturing intrinsic data relationships.

## 2.2 Binary Coding for Image Retrieval

In image retrieval, binary coding offers a transformative advantage. Images, represented by high-dimensional feature vectors, can be mapped to compact binary codes of fixed length. This compression significantly reduces storage requirements and computational complexity, making similarity computations and nearest neighbor searches faster. The binary representation allows for bitwise operations, facilitating efficient retrieval operations on hardware.

## 2.3 Role of Hashing in Large-Scale Image Retrieval

As digital image collections continue to grow exponentially, traditional exhaustive search methods become impractical due to their computational demands. Hashing techniques alleviate this challenge by enabling the transformation of complex data into concise binary codes. These codes facilitate rapid approximate nearest neighbor searches, where only the hash codes rather than the entire
dataset need to be scanned. This efficiency is especially crucial in applications like content-based image retrieval, where users seek similar images from vast repositories.

## 2.4 Challenges and Trade-offs in Hashing

While hashing brings numerous advantages, challenges remain. Hash collisions, where different data points map to the same hash code, can degrade retrieval accuracy. Striking a balance between short hash codes for efficiency and long codes for preserving similarity relationships is a delicate trade-off. The challenge of designing effective hash functions becomes more intricate as the dimensionality of data increases.

## 2.5 Emergence of Deep Learning in Hashing

In recent years, deep learning has revolutionized various fields, and hashing is no exception. The integration of deep neural networks with hashing techniques, known as deep hashing, has opened new avenues for enhancing retrieval accuracy and leveraging the rich representational capabilities of deep architectures. This synergy allows for the extraction of high-level semantic features from images, leading to more meaningful hash codes.

# 3 Deep Supervised Hashing Methods: Strategies and Applications

In this section, we delve into the heart of deep supervised hashing methods. We explore a wide spectrum of strategies employed to harness the power of deep neural networks in improving image retrieval performance. Our analysis covers methodologies, techniques, and innovations that have contributed to the advancement of this domain. We categorize these methods based on their input treatment and provide insights into their respective strengths and limitations.

## 3.1 Input Treatment in Deep Supervised Hashing

Deep supervised hashing methods adopt distinct strategies for processing input data, each yielding unique perspectives on how to extract meaningful features and generate effective hash codes. We classify these strategies into five main categories: pointwise, pairwise, tripletwise, ranking-based, and quantization-based. Each category represents an approach to utilize labeled data to guide the learning process.

**Pointwise Methods.** Pointwise methods treat each training sample individually. They focus on learning a hash function that maps each input image to a binary code while considering its own label. The goal is to optimize the binary codes to align with the individual image-label pairs.

**Pairwise Methods.** Pairwise methods take pairs of images as inputs and aim to learn a hash function that maps similar image pairs to similar hash codes, and dissimilar pairs to dissimilar hash codes. These methods are particularly effective in capturing relative similarities among images.

**Tripletwise Methods.** Tripletwise methods work with triplets of images: an anchor image, a positive image (similar to the anchor), and a negative image (dissimilar to the anchor). The objective is to enforce that the hash code of the anchor is closer to that of the positive image than the negative image. This approach helps in learning discriminative hash codes.
**Ranking-based Methods.** Ranking-based methods focus on preserving the ranking order of pairwise similarities in the hash code space. The aim is to ensure that hash codes reflect the relative similarity ranking among image pairs. These methods are especially suitable for tasks where similarity orders matter, such as ranking-based retrieval.

**Quantization-based Methods.** Quantization-based methods directly optimize the process of converting continuous real-valued features into binary hash codes. These methods aim to minimize the quantization error by strategically mapping real-valued features to binary values, thus improving retrieval performance.

Each of these input treatment strategies offers distinct advantages and addresses specific challenges. In the subsequent sections, we delve into each strategy's methodologies, advancements, and their implications in the context of deep supervised hashing.

## 3.2 Pointwise Methods

Pointwise methods prioritize individual image-label pairs and focus on learning hash functions that align binary codes with each image's label. Unlike relative comparisons between images, these methods concentrate on the classification of each image independently. In Figure 1, an overview of methods in this category is presented. The methodology involves using a CNN to extract features from input images based on their associated labels.

<figure><img src="image_3.png" /><figcaption>Figure 1: Overview of the pointwise category methods.</figcaption></figure>

### 3.2.1 Direct Binary Embedding

Direct Binary Embedding (BDE) (Liu et al., 2017a) employs a ResNet50 trained for classification to achieve hashing functions. This approach aims to learn a representation that closely resembles a binary code, bypassing quantization and its inherent errors. To handle multiple labels, the cross-entropy loss is extended to capture label co-occurrence dependencies while maintaining the independence of each label. This is particularly relevant for real-world images associated with various objects belonging to different categories.

The loss function minimizes the difference between the probability distribution of the actual label and the prediction. It combines cross-entropy softmax and binary sigmoid cross-entropy to model each label independently. This approach allows modeling label relationships and optimizing binary codes more effectively.
### 3.2.2 Deep Multi-Label Hashing

Deep Multi-Label Hashing (DMLH) (Zhong et al., 2017) combines visual content representation from an AlexNet with a label graph to generate binary hashing codes. DMLH tackles challenges posed by diverse and dispersed labels in multi-label image collections. A label co-occurrence graph is constructed, and a semantic partitioning algorithm generates label clusters based on similarities. Semantic vectors are then extracted from images using label clusters, optimizing the Euclidean distance between CNN-generated binary codes and semantic vectors.

DMLH distinguishes itself by capturing label similarities through graph-based partitioning, avoiding complex similarity modeling and facilitating neural network training.

<figure><img src="image_4.png" /><figcaption>Figure 2: The framework of DMLH.</figcaption></figure>

### 3.2.3 Multi-Task Deep Hashing

Multi-Task Deep Hashing (MTDH) (Liang et al., 2017) is tailored for specific domains, such as vehicle-specific multi-label image retrieval. In this method, a CNN (GoogLeNet) predicts three categories per instance: color, model, and vehicle ID. Fully-connected layers, each corresponding to a category, follow the last pooling layer of the CNN. The softmax function is applied to each layer, and the final loss is the sum of softmax losses. This domain-specific approach demonstrates improved performance when using ReLU as the hash layer activation function.

The focus of pointwise methods is on optimizing binary codes for accurate representation of individual image-label pairs, providing insights into each image's unique attributes.

## 3.3 Pairwise Methods

In this section, we explore pairwise methods that use pairs of images to model similarities. These methods build upon different strategies and loss functions to effectively learn hash codes that capture
the relationships between images based on their labels and shared information.

### 3.3.1 Deep Multilevel Semantic Similarity Preserving Hashing (DMSSPH)

DMSSPH, proposed by Wu et al. (2017), introduces a method that addresses the limitation of simple binary similarity definitions. Instead, DMSSPH quantifies similarity using normalized semantic labels and differentiates between very similar, normally similar, and dissimilar pairs. The loss function designed for DMSSPH considers these levels of similarity and aims to maximize separability in the output space while approximating desired discrete values for hash codes. The model utilizes a pairwise loss function that captures both feature learning and hash code generation in a balanced manner.

<figure><img src="image_5.png" /><figcaption>Figure 3: Overview of the proposed framework of DMSSPH.</figcaption></figure>

### 3.3.2 Deep Multi-similarity Hashing (DMSH)

Li et al. (2017) present DMSH, which extends the concept of capturing multiple levels of similarity. Rather than defining similarity categories, DMSH integrates the similarity rankings based on shared labels. This method uses a pairwise function to induce a CNN to learn the relationships between image pairs, where the degree of similarity is determined by the number of labels they have in common. DMSH formulates a loss function that minimizes the discrepancy between image pairs and leverages the effectiveness of multi-level similarity modeling.

### 3.3.3 Instance Similarity Deep Hashing (ISDH)

ISDH, introduced by Zhang et al. (2018), addresses the challenge of defining similarity when images share multiple labels. Instead of categorizing similarity into binary values, ISDH proposes a soft definition of similarity based on normalized semantic labels. This method uses a posteriori probability to model similarity relationships and optimize hash codes. By incorporating the uniqueness of image sets, ISDH refines the ranking response, providing a more accurate measure of similarity.
<figure><img src="image_6.png" /><figcaption>Figure 4: The architecture of DMSH.</figcaption></figure>

## 3.4 Tripletwise Methods

Tripletwise methods focus on generating hash codes using the relative comparisons between three data points: a query image, a similar image, and a dissimilar image. This approach aims to ensure that the similarity between the query image and the similar image is greater than the similarity between the query image and the dissimilar image.

### 3.4.1 Instance-Aware Hashing (IAH)

Instance-Aware Hashing (IAH), proposed by Lai et al. (2016), introduces a novel approach to multi-label image retrieval. It acknowledges that images may contain objects of various categories, unlike traditional methods that represent an image as a whole. IAH employs a four-module architecture:

* **Region Proposal Generation Module:** Uses the Geodesic Object Proposal (GOP) algorithm to create region proposals for input images, indicating regions of interest.

* **Neural Convolution Network Module:** Utilizes a GoogLeNet to extract features from generated region proposals, employing a Spatial Pyramid Pooling (SPP) scheme to handle the large number of proposals.

* **Label Probability Calculation Module:** Calculates label probabilities for each region proposal using the combined matrix of intermediate features and a fully connected layer.

* **Hashing Encoding Module:** Converts image representations into hash codes, incorporating instance-aware image representations for multi-label images organized into categories.

### 3.4.2 Triplet-Based Deep Binary Embedding (TDBE)

Zhuang et al. (2016) present Triplet-Based Deep Binary Embedding (TDBE), which addresses the computational complexity of binary code learning using triplet functions. TDBE employs a two-step collaborative approach:
* **High-Order Inference Task:** Converts a triplet loss function into a set of pairwise terms, representing the relations between training points using a weight matrix.

* **Deep Neural Network (DNN) Learning:** Utilizes a DNN to map images to the binary code space, optimizing the structure of hashing incrementally in groups of bits.

### 3.4.3 Deep Supervised Hashing with Triplet Labels (DTSH)

DTSH, introduced by Wang, Shi, and Kitani in 2016, is a deep hashing method that leverages triplet labels to learn image features and hash codes simultaneously. The method is inspired by DPSH and aims to maximize the likelihood of given triplet labels. The main contributions of DTSH are as follows:

* **End-to-End Learning:** A unified approach for image feature and hash code learning in an end-to-end manner using triplet labels.

* **Likelihood Formulation:** A unique formulation of the likelihood of the given triplet labels as an evaluation metric for the quality of learned hash codes.

* **Ablative Analysis:** An ablative analysis of the loss function, providing insights into how each term contributes to overall performance.

* **State-of-the-Art Performance:** Achieves state-of-the-art performance on benchmark datasets, surpassing existing triplet label-based deep hashing methods.

<figure><img src="image_7.png" /><figcaption>Figure 5: Overview of the proposed end-to-end deep hashing method DTSH.</figcaption></figure>

## 3.5 Ranking-based Methods

Ranking-based methods concentrate on preserving the ranking order of pairwise similarities within the hash code space. This approach ensures that hash codes accurately represent the relative similarity ranking among pairs of images. Such methods are particularly effective for tasks where
similarity order matters, like ranking-based retrieval, which aims to retrieve images in an order corresponding to their relevance to a query image.

These methods frequently employ ranking loss functions that quantify the extent to which the order of similarity ranks is maintained in the hash code space. By optimizing these loss functions, the neural network learns to generate hash codes that uphold the desired ranking of image pairs.

### 3.5.1 Deep Semantic Ranking Based Hashing (DSRH)

DSRH (Zhao et al., 2015) presents a semantic ranking approach to guide deep hash learning and preserve multiple levels of similarity between multi-label images. Concretely:

* **Semantic Similarity Levels:** Given a query image $q$, DSRH computes a semantic similarity level $r$ for each image $x$ in the dataset based on the number of shared labels; more shared labels yield higher levels (stronger similarity).
* **Ranking Preservation:** The method maintains the ranking order of neighbors for each data point according to its semantic labels, using the ranking as an evaluation target for the hash function.
* **Loss Function:** A surrogate ranking loss over sets of triplets encourages Hamming distances for similar pairs to be smaller than for dissimilar pairs.
* **Adaptive Weighting:** Similarity-level-dependent weights prioritize ranking precision for more relevant items.

<figure><img src="image_8.png" /><figcaption>Figure 6: The proposed deep semantic ranking of DSRH.</figcaption></figure>
## 3.6 Quantization-based Methods

Quantization-based methods offer a direct approach to address the conversion of continuous real-valued features into binary hash codes. The primary goal is to minimize quantization error, which arises from representing continuous features with discrete binary codes. By strategically mapping real-valued features to binary values, these methods aim to enhance retrieval performance by reducing information loss during quantization. Quantization-based methods commonly incorporate quantization loss functions to guide learning, encouraging mappings whose binary realizations preserve meaningful information.

### 3.6.1 Quantization-Based Hashing (QBH)

In large-scale recommendation and retrieval settings, Quantization-Based Hashing adopts a two-stage framework—*similarity quantization* and *norm quantization*—to reduce quantization error while determining optimal bit budgets. The first stage learns compact binary attributes that approximate real-valued similarities (minimizing quantization loss); the second stage controls code norms to further stabilize code distribution, improving efficiency and accuracy.

### 3.6.2 Central Similarity Quantization (CSQ)

CSQ (2020) addresses the limitations of hashing that learns from localized pairwise/triplet relations by introducing a global *center similarity*. Codes of similar data pairs are pulled toward a shared center, while dissimilar pairs are pushed toward distinct centers. Well-separated hash centers in Hamming space (e.g., built via Hadamard matrices or Bernoulli schemes) reduce collisions and enhance retrieval accuracy and efficiency.
<figure><img src="image_9.png" /><figcaption>Figure 7: Pairwise/triplet similarity based hashing methods and the proposed center similarity quantization.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 8: Visualization of hash codes generated by four deep hash learning methods using different data similarity metrics, trained on MNIST with ten groups of data points (one class per group).</figcaption></figure>
# 4 Applications of Deep Supervised Hashing

## 4.1 Content-Based Image Retrieval

Content-based image retrieval (CBIR) remains a cornerstone in the realm of image search and retrieval. It involves the retrieval of images from a database based on their visual content, which encompasses attributes such as color, texture, and shape. In the context of CBIR, deep supervised hashing methods emerge as pivotal tools, reshaping the landscape of image retrieval systems by producing compact and semantically meaningful hash codes. These codes serve as powerful descriptors, expediting the retrieval process by swiftly identifying visually similar images from extensive databases.

<figure><img src="image_11.png" /><figcaption>Figure 9: The hierarchical categories of the hash-based methods where methods in the same color belong to the same category.</figcaption></figure>

As the field of CBIR continues to evolve, the integration of deep supervised hashing methods into its framework opens doors to novel possibilities. These methods not only expedite the retrieval process but also contribute to the development of more accurate and efficient image search systems. The synergy between deep learning and content-based image retrieval holds promise for addressing the challenges posed by ever-growing image databases and the need for faster, more reliable image retrieval solutions.

**Content-Based Image Retrieval (continued).** Content-based image retrieval (CBIR) remains a cornerstone in image search and retrieval. It involves retrieving images from a database based on visual content (color, texture, shape). In CBIR, deep supervised hashing produces compact and semantically meaningful hash codes that accelerate the search and increase precision over large-scale collections, making indexing and nearest-neighbor retrieval feasible at web scale.

## 4.2 Cross-Modal Retrieval

Cross-modal retrieval stands at the intersection of various modalities, enabling the search for data in one modality based on a query from another modality. This dynamic approach has gained significant attention with the proliferation of multimodal data (e.g., text-to-image search). Given the distinct characteristics of different modalities, measuring content similarity across modalities is challenging.

Deep supervised hashing techniques extend effectively to cross-modal retrieval by mapping heterogeneous modalities into a shared Hamming space, so that similarity can be computed efficiently
and compatibly. This harmonization bridges the semantic gap across modalities, enabling fast and accurate multimedia information retrieval and image-to-text (and text-to-image) matching.

<figure><img src="image_12.png" /><figcaption>Figure 10: The basic idea of cross-modal hashing: different modalities are projected into a common Hamming space for fast retrieval.</figcaption></figure>

## 4.3 Face Recognition

The landscape of face recognition has undergone a profound transformation through the application of deep supervised hashing methods. Since the breakthroughs of 2014 (e.g., DeepFace, DeepID), deep learning has enabled hierarchical feature representations that are robust to pose, illumination, and expression. Within this context, deep supervised hashing learns compact yet discriminative binary codes that encapsulate identity-specific information, supporting rapid matching, verification, and identification over large galleries.

Such representations are especially valuable for security systems, access control, large-scale de-duplication, and personalized user experiences, where latency and memory usage are critical. By compressing discriminative embeddings into short codes with preserved semantics, face search becomes both fast and memory-efficient.
<figure><img src="image_13.png" /><figcaption>Figure 11: DCDH framework for face recognition with hash codes.</figcaption></figure>

# 5 Challenges and Future Directions

Despite significant advances, several challenges remain for deep supervised hashing to reach its full potential in practical environments.

## 5.1 Data Imbalance and Label Noise

The success of deep supervised hashing hinges on accurately labeled training data to learn effective hash functions. Real-world datasets, however, often exhibit both class imbalance and label noise, which can degrade code quality and retrieval performance.

**Imbalanced Datasets.** When certain classes are underrepresented, models can become biased toward dominant classes and generalize poorly for rare categories. Effective strategies include reweighting losses, resampling, class-balanced focal objectives, and curriculum designs that ensure minority classes contribute sufficient signal to the code learning process.

**Label Noise.** Perfectly clean labels are rare. Annotation errors and inherent ambiguities introduce noise that corrupts supervision and distorts similarity targets. Robust learning under noise involves identifying reliable samples, modeling annotation uncertainty, bootstrapping with noise-tolerant objectives, and employing consistency regularization. Noise-aware hashing (e.g., sample selection and reweighting) helps maintain discriminative binary codes under imperfect labels.

## 5.2 Scalability to Large-Scale Databases

As datasets grow to tens or hundreds of millions of images, scalability becomes central. Many supervised methods rely on pairwise or triplet objectives whose naive construction scales quadratically or cubically, making similarity matrices intractable. Quantizing continuous solutions post hoc can also introduce non-negligible quantization error.
To scale, practical systems exploit efficient pair/triplet sampling, memory-efficient ranking losses, distributed training, and hardware acceleration. Center/cluster-based objectives (e.g., CSQ) amortize supervision signals, while product quantization/codebooks and multi-index hashing accelerate search at query time. The goal is to preserve retrieval quality while keeping training and inference costs manageable.

<figure><img src="image_14.png" /><figcaption>Figure 12: Visualisation of a large-scale database of images.</figcaption></figure>

# 6 Conclusion

In this paper, we have embarked on a journey through the realm of deep supervised hashing methods for image retrieval, seeking to provide a structured and insightful perspective. While the expanse of research and innovation in this domain is immense, our endeavor has been to categorize and distill this wealth of knowledge into a coherent overview, one that facilitates understanding for both seasoned researchers and those venturing into the field.

Throughout our exploration, we have witnessed the transformative capabilities of deep supervised hashing methods. These techniques offer an avenue to bridge the gap between high-dimensional image data and efficient retrieval, laying the foundation for advancements in content-based image retrieval, cross-modal matching, and face recognition. The potency of these methods lies in their ability to distill intricate visual patterns into compact hash codes, thus rendering large-scale image retrieval tasks computationally efficient and semantically meaningful.

However, this journey has not been without its challenges. Our analysis of data imbalance, label noise, and scalability concerns has underscored the pressing need for further research. Addressing these challenges is not merely a task for the present, but an investment in the future. The resilience of deep supervised hashing methods in the face of noisy labels and their capacity to scale seamlessly to vast datasets will define their continued relevance and impact.

Importantly, it is worth noting that while we have endeavored to categorize and summarize a significant body of work, our coverage is by no means exhaustive. The ingenuity and dedication of researchers have birthed a multitude of innovations that expand the boundaries of what is possible. We hope that our classification and insights will serve as a guide for researchers, offering a compass to navigate the intricacies of this dynamic and ever-evolving landscape.

In closing, the fusion of deep learning with supervised hashing methods holds the promise of reshaping how we retrieve, match, and recognize images across a plethora of applications. As the torchbearers of this field, we remain poised to explore, innovate, and pave the way for a future where image retrieval transcends its current limitations.
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