Image Retrieval Model Research Articles

Modelling of an Intelligent and Secured Image Retrieval Model by Employing Deep Belief Network

Modelling of an Intelligent and Secured Image Retrieval Model by Employing Deep Belief Network

Content-Based Image Retrieval and Feature Extraction: Analysing the Literature

A significant amount of multimedia data consists of digital images, and multimedia content analysis is used in many real-world computer vision applications. Multimedia information, especially photos, has become much more complicated in the last several years. Every day, millions of photos are posted to various websites, such as Instagram, Facebook, and Twitter. Finding a suitable image in an archive is a difficult research subject for the field of computer vision. Most search engines use standard text-based techniques that depend on metadata and captions in order to fetch photos. Over the past 20 years, a great deal of research has been conducted on content-based image retrieval (CBIR), picture categorization, and analysis. In image classification models and CBIR, high-level picture representations are represented as feature vectors made up of numerical values. Empirical evidence indicates a considerable disparity between picture feature representation and human visual understanding. Reducing the semantic gap between human visual understanding and picture feature representation is the aim of this study. This study aims to do a thorough analysis of the latest advancements in the domains of Content-Based picture Retrieval and picture representation. We performed a comprehensive analysis of many models for image retrieval and picture representation, encompassing the most recent advancements in semantic deep-learning methods and feature extraction. This paper provides an in-depth analysis of the key ideas and important studies related to image representation and content-based picture retrieval. In an effort to stimulate more research in this field, it also offers a preview of potential future study topics.

Retraction Note: Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

Retraction Note: Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

DSCEH: Dual-Stream Correlation-Enhanced Deep Hashing for Image Retrieval

Deep Hashing is widely used for large-scale image-retrieval tasks to speed up the retrieval process. Current deep hashing methods are mainly based on the Convolutional Neural Network (CNN) or Vision Transformer (VIT). They only use the local or global features for low-dimensional mapping and only use the similarity loss function to optimize the correlation between pairwise or triplet images. Therefore, the effectiveness of deep hashing methods is limited. In this paper, we propose a dual-stream correlation-enhanced deep hashing framework (DSCEH), which uses the local and global features of the image for low-dimensional mapping and optimizes the correlation of images from the model architecture. DSCEH consists of two main steps: model training and deep-hash-based retrieval. During the training phase, a dual-network structure comprising CNN and VIT is employed for feature extraction. Subsequently, feature fusion is achieved through a concatenation operation, followed by similarity evaluation based on the class token acquired from VIT to establish edge relationships. The Graph Convolutional Network is then utilized to enhance correlation optimization between images, resulting in the generation of high-quality hash codes. This stage facilitates the development of an optimized hash model for image retrieval. In the retrieval stage, all images within the database and the to-be-retrieved images are initially mapped to hash codes using the aforementioned hash model. The retrieval results are subsequently determined based on the Hamming distance between the hash codes. We conduct experiments on three datasets: CIFAR-10, MSCOCO, and NUSWIDE. Experimental results show the superior performance of DSCEH, which helps with fast and accurate image retrieval.

Deep Attention Fusion Hashing (DAFH) Model for Medical Image Retrieval.

To address this, we propose a novel deep learning-based hashing model, the Deep Attention Fusion Hashing (DAFH) model, which integrates advanced attention mechanisms with medical imaging data. The DAFH model enhances retrieval performance by integrating multi-modality medical imaging data and employing attention mechanisms to optimize the feature extraction process. Utilizing multimodal medical image data from the Cancer Imaging Archive (TCIA), this study constructed and trained a deep hashing network that achieves high-precision classification of various cancer types. At hash code lengths of 16, 32, and 48 bits, the model respectively attained Mean Average Precision (MAP@10) values of 0.711, 0.754, and 0.762, highlighting the potential and advantage of the DAFH model in medical image retrieval. The DAFH model demonstrates significant improvements in the efficiency and accuracy of medical image retrieval, proving to be a valuable tool in clinical settings.

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Background/Objectives: The MRI has proven to be extremely effective in detecting tumors, with millions of images created each day throughout the world. To find similar images from a vast collection, Content-Based Tumor Image Retrieval (CBTIR) technology has been used to analysis the medical image. In the traditional retrieval methods, retrieving a similar image from the large database is crucial task. To overcome this issue we developed deep learning based retrieval method. Methods: This research offers a retrieval approach based on predefined ResNet models for quick and accurate image retrieval. We tested various prominent ResNet models with different distance similarity metrics, and the best option was determined by this work. Findings: After the various evaluation of ResNet models with varied distance measures on the CE-MRI data set, ResNet50 model applied with Hamming distance yields 99.33% of retrieval precision. Novelty: This work used predefined ResNet models with the combination of Distance similarity metrics to achieve more accurate results on medical image retrieval compared to the other conventional methods. Keywords: Content Based Image Retrieval, Tumor Retrieval, Hamming Distance, Euclidean Distance, Minkowski

An improved deep hashing model for image retrieval with binary code similarities

The exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms.

An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features

In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (LC2R-Net) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that LC2R-Net achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of LC2R-Net in handling the task of lunar complex impact crater image retrieval.

Deep Representation-Based Fuzzy Graph Model for Content-Based Image Retrieval

Deep Representation-Based Fuzzy Graph Model for Content-Based Image Retrieval

Integrated Design of Building Environment Based on Image Segmentation and Retrieval Technology

Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation and retrieval based on an image segmentation network. Primarily, spatial attention is incorporated into the U-Net segmentation network to enhance the extraction of image features. Subsequently, a dual-path attention mechanism is integrated into the U-Net backbone network, facilitating the seamless integration of information across different spaces and scales. Experimental results showcase the superior performance of the proposed model on the test set, with average dice coefficient, accuracy, and recall reaching 94.67%, 95.61%, and 97.88%, respectively, outperforming comparative models. The proposed model can enhance the U-Net network's capability to identify targets within feature maps. The amalgamation of image segmentation networks and attention mechanisms in artificial intelligence technology enables precise segmentation and retrieval of architectural images.

FFLDGA-Net: Image retrieval method based on Feature Fusion Learnable Descriptor Graph Attention Network

Image retrieval aims to retrieve and return the image in the database that is most similar to the query image. However, the performance of image retrieval models is often hindered by the limited dimensionality of images, which lacks depth information about objects. To address this issue, we propose a novel image retrieval model called FFLDGA-Net (Feature Fusion-based Learnable Descriptor Graph Attention Network). This model aims to overcome the absence of depth information in images by fusing feature information from both image data and point cloud data. First, we introduce the LDGA-Net, which effectively improved the model’s ability to mine hard samples and negative samples. Then, we combine the multi-scale route convolution module with a one-dimensional path aggregation network to fuse point clouds and image features at multiple scales, and establish a high-dimensional relationship between features and low-dimensional features. To mitigate training noise, we incorporate a soft label strategy tailored to the dataset’s characteristics. Our experimental results on two benchmark datasets demonstrate the significant improvements achieved by FFLDGA-Net in image retrieval performance.

Multimodal learning with only image data: A deep unsupervised model for street view image retrieval by fusing visual and scene text features of images

AbstractAs one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval.

Exploring the Impact of Denoising Autoencoder Architectures on Image Retrieval

In today’s busy world, people are shifting to online shopping for their basic needs like groceries, clothes, etc. As a result, modern applications are incorporating image search as part of their application. However, retrieving accurate fashion images is challenging due to the low inter-class separability. In this paper, we aim to propose and implement three deep learning denoising autoencoder image retrieval models trained on Fashion-MNIST dataset and MNIST dataset. Fashion-MNIST is widely used datasets to evaluate model because of its low inter-class separability. It is difficult to get a good accuracy on Fashion-MNIST dataset. The proposed models train to learn significant features of fashion images and improve the precision of image retrieval. These models are compared based on the Label Ranking Average Precision (LRAP) values, with the third model bagging the highest LRAP value. These models also trained and tested on MNIST dataset, which have similar configuration as Fashion-MNIST dataset but with high inter-class separability. Intriguingly, the second and third models trained on MNIST dataset demonstrate neck-to-neck performance, both achieving comparable accuracy. This choice of dataset ensures that the models’ accuracy is more noticeable and significant. The outcomes of this study validate the efficacy of denoising autoencoders in fashion image retrieval and emphasize their prospective in improving online shopping experiences.

Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

Spatial Pyramid Attention Enhanced Visual Descriptors for Landmark Retrieval

Landmark retrieval, which aims to search for landmark images similar to a query photo within a massive image database, has received considerable attention for many years. Despite this, finding landmarks quickly and accurately still presents some unique challenges. To tackle these challenges, we present a deep learning model, called the Spatial-Pyramid Attention network (SPA). This network is an end-to-end convolutional network, incorporating a spatial-pyramid attention layer that encodes the input image, leveraging the spatial pyramid structure to highlight regional features based on their relative spatial distinctiveness. An image descriptor is then generated by aggregating these regional features. According to our experiments on benchmark datasets including Oxford5k, Paris6k, and Landmark-100, our proposed model, SPA, achieves mean Average Precision (mAP) accuracy of 85.3% with the Oxford dataset, 89.6% with the Paris dataset, and 80.4% in the Landmark-100 dataset, outperforming existing state-of-theart deep image retrieval models.

Efficient rotated and scaled digital image retrieval model using deep learning-based hybrid features extraction

Efficient rotated and scaled digital image retrieval model using deep learning-based hybrid features extraction

Multi-Scale Feature Fusion Based on PVTv2 for Deep Hash Remote Sensing Image Retrieval

For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully express the complexity of the image information. Due to the large volume of remote sensing images, retrieval requires extensive memory and time. Hence, the problem of how to organically fuse multi-scale features and enhance retrieval efficiency is yet to be resolved. We propose an end-to-end deep hash remote sensing image retrieval model (PVTA_MSF) by fusing multi-scale features based on the Pyramid Vision Transformer network (PVTv2). We construct the multi-scale feature fusion module (MSF) by using a global attention mechanism and a multi-head self-attention mechanism to reduce background interference and enhance the representation capability of image features. Deformable convolution is introduced to address the challenge posed by varying target orientations. Moreover, an intra-class similarity (ICS) loss is proposed to enhance the discriminative capability of the hash feature by minimizing the distance among images of the same category. The experimental results show that, compared with other state-of-the-art methods, the proposed hash feature could yield an excellent representation of remote sensing images and improve remote sensing image retrieval accuracy. The proposed hash feature can gain an increase of 4.2% and 1.6% in terms of mAP on the UC Merced and NWPU-RESISC45 datasets, respectively, in comparison with other methods.

A novel Siamese deep hashing model for histopathology image retrieval

A novel Siamese deep hashing model for histopathology image retrieval

Construction Models for Image Sketching and Retrieval: A Systematic Review

Introduction: Image searching is a continual challenge even with the many image retrieval models that have sprung up. Sketch-Based Image Retrieval (SBIR) models attempt to solve this challenge by searching using sketching. The existing SBIR algorithms have limited performance because of ambiguities and variations in hand-drawn sketches. Aims: The aim of this work was to review and identify the strengths and weaknesses of the existing SBIR models. Materials and Methods: Articles were selected from Google Scholar assessing strictly sketch construction models. Search terms include sketch construction, sketch-based image retrieval, hypermedia, multimedia, design strategies, and algorithms. Results: The search returned 455 articles of which only 134 studies met the inclusion criteria. 30 papers were on Convolutional Neural Network (CNN) and hybrids. 6 on Contour and Stroke Segments. 4 on Generative Adversarial Network while 3 papers were on Deep Hashing. 6 papers reported use of 3D-CNN-based methods while 85 papers used other methods like sparse coding and bag of regions. Accuracy, recall and precision ranged from 59.47% to 99.4%, 20.10% to 47.70% and 33.40% to 51.00% respectively. Conclusion: There are some promising SBIR models but lots of effort is required if computational SBIRs are to be adopted. Most studies did not include any performance metric which makes it difficult to assess the performances of the algorithms proposed. Researchers are advised to always report the performance algorithms. The future plan is to develop a robust SBIR algorithm which will accommodate handwriting ambiguity variations

Aggregating Deep Features of Multi-CNN Models for Image Retrieval

Aggregating Deep Features of Multi-CNN Models for Image Retrieval