Content-based Image Retrieval Research Articles

Using Deep Learning and Cbir To Address Copyright Concerns of AI-Generated Art: A Systematic Literature Review

This systematic literature review explores the intersection of deep learning and content-based image retrieval (CBIR) in addressing copyright concerns related to AI-generated art. As artificial intelligence rapidly transforms various artistic domains, it raises critical questions regarding authorship, ownership, and the ethical implications of machine-generated creativity. The review examines the capabilities of CBIR systems in identifying AI-generated images by analyzing visual features such as color, texture, and shape. Additionally, it highlights the role of deep learning models in enhancing the accuracy of these systems through the detection of distinctive patterns characteristic of AI artworks. The findings underscore the importance of developing robust methodologies that leverage AI and CBIR technologies to protect intellectual property rights while fostering innovation in the creative industries. This research contributes to the broader discourse on the legal and ethical challenges posed by AI in art, providing insights for policymakers, artists, and technologists in navigating the evolving landscape of AI-generated content.

An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

Retraction Note: Analytic hierarchy process-based automatic feature weight assignment method for content-based satellite image retrieval system

Retraction Note: Analytic hierarchy process-based automatic feature weight assignment method for content-based satellite image retrieval system

An Image-based Search for Pulsar Candidates in the MeerKAT Bulge Survey

We report on the results of an image-based search for pulsar candidates toward the Galactic bulge. We used mosaic images from the MeerKAT radio telescope that were taken as part of a 173 deg2 survey of the bulge and Galactic center of our Galaxy at L band (856–1712 MHz) in all four Stokes I, Q, U, and V. The image rms noise levels of 12–17 μJy ba−1 represent a significant increase in sensitivity over past image-based pulsar searches. Our primary search criterion was circular polarization, but we used other criteria, including linear polarization, in-band spectral index, compactness, variability, and multiwavelength counterparts to select pulsar candidates. We first demonstrate the efficacy of this technique by searching for polarized emission from known pulsars and comparing our results with measurements from the literature. Our search resulted in a sample of 75 polarized sources. Bright stars or young stellar objects were associated with 28 of these sources, including a small sample of highly polarized dwarf stars with pulsar-like steep spectra. Comparing the properties of this sample with the known pulsars, we identified 30 compelling candidates for pulsation follow-up, including two sources with both strong circular and linear polarization. The remaining 17 sources are either pulsars or stars, but we cannot rule out an extragalactic origin or image artifacts among the brighter, flat-spectrum objects.

PERBANDINGAN JARAK EUCLIDEAN, CITYBLOCK, MINKOWSKI, CANBERRA, DAN CHEBYSHEV DALAM SISTEM TEMU KEMBALI CITRA BATIK

Batik is a highly valuable cultural heritage in Indonesia, showcasing a rich diversity of motifs with deep meaning and aesthetics. To enhance the accessibility and utilization of batik collections, an efficient image retrieval system is essential. This study compares distance measurement methods in a batik image retrieval system: Euclidean, Cityblock, Minkowski, Canberra, and Chebyshev, using a combination of color and texture features. The dataset comprises 50 types of batik images. The results show that the Cityblock method achieves the highest Mean Average Precision (MAP) of 97.71, followed by Canberra with MAP 96.87. The Euclidean method also performs well with a MAP of 94.56, while Minkowski and Chebyshev have lower MAP values of 92.93 and 90.89, respectively. Chebyshev experiences the largest MAP drop when images are rotated (5.98), while Cityblock demonstrates the best resistance to rotation with the smallest MAP drop (1.51). This research successfully developed a Content-Based Image Retrieval (CBIR) system with a GUI in MATLAB and suggests integrating the latest image processing and machine learning techniques for further enhancement.

Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

Feature-level Fusion vs. Score-level Fusion for Image Retrieval Based on Pre-trained Deep Neural Networks

Today’s complex multimedia content made retrieving images similar to the user’s query from the database a challenging task. The performance of a Content-Based Image Retrieval System (CBIR) system highly depends on the image representation in a form of low-level features and similarity measurement. The traditional visual descriptors that do not provide good prior domain knowledge could lead to poor performance retrieval results. On the other hand, Deep Convolutional Neural Networks (DCNNs) have recently achieved a remarkable success as methods for image classification in various domains. Recently, pre-trained deep convolution neural networks on thousands of classes have the ability to extract very accurate and representative features which, in addition to classification, can also be successfully used in image retrieval systems. ResNet152, GoogLeNet and InceptionV3 are some of the effective and successful examples of pre-trained DCNNs recently applied in a computer vision tasks such as object recognition, clustering, and classification. In this paper, two approaches for a CBIR system, namely early fusion and late fusion, have been presented and compared. The early fusion utilizes concatenation of the features extracted by each possible pair of DCNNs, that is ResNet152-GoogLeNet, ResNet152-InceptionV3, and GoogLeNet-InceptionV3, and the late fusion apply CombSum method with Z-Score standardization to combine the score results provided by each DCNN of the aforementioned pairs. In the experiments on a popular WANG dataset it has been shown that late fusion approach slightly outperforms early fusion approach. The best performance of our experiments in terms of Average Precision (AP) for the top 20 results reaches 96.82%.

A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm

With the rapid development of remote sensing image data, the efficient retrieval of target images of interest has become an important issue in various applications including computer vision and remote sensing. This research addressed the low-accuracy problem in traditional content-based image retrieval algorithms, which largely rely on comparing entire image features without capturing sufficient semantic information. We proposed a scene graph similarity-based remote sensing image retrieval algorithm. Firstly, a one-shot object detection algorithm was designed for remote sensing images based on Siamese networks and tailored to the objects of an unknown class in the query image. Secondly, a scene graph construction algorithm was developed, based on the objects and their attributes and spatial relationships. Several construction strategies were designed based on different relationships, including full connections, random connections, nearest connections, star connections, or ring connections. Thirdly, by making full use of edge features for scene graph feature extraction, a graph feature extraction network was established based on edge features. Fourthly, a neural tensor network-based similarity calculation algorithm was designed for graph feature vectors to obtain image retrieval results. Fifthly, a dataset named remote sensing images with scene graphs (RSSG) was built for testing, which contained 929 remote sensing images with their corresponding scene graphs generated by the developed construction strategies. Finally, through performance comparison experiments with remote sensing image retrieval algorithms AMFMN, MiLaN, and AHCL, in precision rates, Precision@1 improved by 10%, 7.2%, and 5.2%, Precision@5 improved by 3%, 5%, and 1.7%; and Precision@10 improved by 1.7%, 3%, and 0.6%. In recall rates, Recall@1 improved by 2.5%, 4.3%, and 1.3%; Recall@5 improved by 3.7%, 6.2%, and 2.1%; and Recall@10 improved by 4.4%, 7.7% and 1.6%.

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.

COMPUTER VISION METHODS FOR CONDUCTING OSINT INVESTIGATIONS

This paper researches the application of computer vision techniques in concern with OSINT (Open Source Intelligence) investigations. It explores how state-ofthe- art algorithms and models in computer vision can be used to automate and enhance the process of gathering, analyzing, and interpreting visual data from open sources. The research focuses on the critical steps of image scraping, data preprocessing, and embedding generation using advanced deep learning models such as CLIP. Additionally, the study examines the challenges of managing large-scale visual data and implementing efficient search mechanisms through vector databases like Faiss and Weaviate. By applying these technologies, the paper illustrates how investigators can improve the accuracy and efficiency of imagebased searches, which are later used for uncovering hidden connections and verifying information in OSINT investigations. The findings contribute to the growing field of computer vision and intelligence gathering, offering practical recommendations for enhancing investigative processes through the integration of computer vision methodologies.

SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud

The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.

A comprehensive guide to content-based image retrieval algorithms with visualsift ensembling.

Content-based image retrieval (CBIR) systems are vital for managing the large volumes of data produced by medical imaging technologies. They enable efficient retrieval of relevant medical images from extensive databases, supporting clinical diagnosis, treatment planning, and medical research. This study aims to enhance CBIR systems' effectiveness in medical image analysis by introducing the VisualSift Ensembling Integration with Attention Mechanisms (VEIAM). VEIAM seeks to improve diagnostic accuracy and retrieval efficiency by integrating robust feature extraction with dynamic attention mechanisms. VEIAM combines Scale-Invariant Feature Transform (SIFT) with selective attention mechanisms to emphasize crucial regions within medical images dynamically. Implemented in Python, the model integrates seamlessly into existing medical image analysis workflows, providing a robust and accessible tool for clinicians and researchers. The proposed VEIAM model demonstrated an impressive accuracy of 97.34% in classifying and retrieving medical images. This performance indicates VEIAM's capability to discern subtle patterns and textures critical for accurate diagnostics. By merging SIFT-based feature extraction with attention processes, VEIAM offers a discriminatively powerful approach to medical image analysis. Its high accuracy and efficiency in retrieving relevant medical images make it a promising tool for enhancing diagnostic processes and supporting medical research in CBIR systems.

A New Approach for Effective Retrieval of Medical Images: A Step towards Computer-Assisted Diagnosis.

The biomedical imaging field has grown enormously in the past decade. In the era of digitization, the demand for computer-assisted diagnosis is increasing day by day. The COVID-19 pandemic further emphasized how retrieving meaningful information from medical repositories can aid in improving the quality of patient's diagnosis. Therefore, content-based retrieval of medical images has a very prominent role in fulfilling our ultimate goal of developing automated computer-assisted diagnosis systems. Therefore, this paper presents a content-based medical image retrieval system that extracts multi-resolution, noise-resistant, rotation-invariant texture features in the form of a novel pattern descriptor, i.e., MsNrRiTxP, from medical images. In the proposed approach, the input medical image is initially decomposed into three neutrosophic images on its transformation into the neutrosophic domain. Afterwards, three distinct pattern descriptors, i.e., MsTrP, NrTxP, and RiTxP, are derived at multiple scales from the three neutrosophic images. The proposed MsNrRiTxP pattern descriptor is obtained by scale-wise concatenation of the joint histograms of MsTrP×RiTxP and NrTxP×RiTxP. To demonstrate the efficacy of the proposed system, medical images of different modalities, i.e., CT and MRI, from four test datasets are considered in our experimental setup. The retrieval performance of the proposed approach is exhaustively compared with several existing, recent, and state-of-the-art local binary pattern-based variants. The retrieval rates obtained by the proposed approach for the noise-free and noisy variants of the test datasets are observed to be substantially higher than the compared ones.

Bi-level feature Classification Approach in Privacy Preserving Cloud Network for High-Performance Content-based Image retrieval Mechanism

Bi-level feature Classification Approach in Privacy Preserving Cloud Network for High-Performance Content-based Image retrieval Mechanism

Cross-modality sub-image retrieval using contrastive multimodal image representations

In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with robust feature extraction and bag-of-words models for efficient and reliable retrieval. We illustrate its advantages through a replacement study, exploring a number of feature extractors and learned representations, as well as through comparison to recent (cross-modality) CBIR methods. For the task of (sub-)image retrieval on a (publicly available) dataset of brightfield and second harmonic generation microscopy images, the results show that our approach is superior to all tested alternatives. We discuss the shortcomings of the compared methods and observe the importance of equivariance and invariance properties of the learned representations and feature extractors in the CBIR pipeline. Code is available at: https://github.com/MIDA-group/CrossModal_ImgRetrieval.

Design and Implementation of a Mobile E-Commerce Platform Based-on Machine Learning

In this paper, the “STORY”, which is an e-commerce mobile platform, is developed based-on machine learning techniques in order to consolidate selling methods into a unified, efficient, and personalized environment. Also, to elevate user experiences, and cultivate a thriving e-commerce environment in Libya. STORY is designed to serve the needs of both customers and merchants, aiming to bridge the gap between consumer demands and efficient business interactions. This platform strives to provide customers with a wide range of services and functions that match their preferences, while empowering merchants with a user-friendly mobile interface for effective business management. Among its notable features, STORY boasts an image-based search function, which simplifies the process of searching for desired or similar products. Additionally, the platform integrates an intelligent recommendation system capable of tracking and analyzing customer behaviors within the platform. This system collects pertinent data to offer personalized product recommendations and tailored offers, elevating the shopping experience. The system underwent an evaluation via a questionnaire, which had overwhelmingly positive results. User feedback and satisfaction regarding the system's functionalities and performance were notably favorable.

Content-based image retrieval by classification with reinforcement optimisation evolutionary machine learning with applications

ABSTRACT Content Based Image Retrieval (CBIR) plays a significant role in identifying the similarity of images with large datasets. It is identified based on the size, colour, and texture features of the image. But in such conditions, it is complex to determine the features of query images in large datasets and does not show accurate similarity when compared with every image in the retrieval process. In order to perform an efficient similarity of images, a novel Machine Learning (ML) approach Kernelized Radial Basis Auto-Encoder Function Neural Network (Ker_RadBAEFNN) technique is proposed that performs the individual image classification in the retrieval process. Moreover, the neural networks are optimised based on the reinforcement process and perform the extraction process regarding individual images. Further,reinforcement-based optimisation estimates the images in neural networks for undertaking an automatic feature extraction of query images. The performance of the classification process is validated based on MNIST, METU, and COCO datasets that determined the efficiency of the recognition and classification process of image retrieval. The experimental analysis is carried out based on various measures such as accuracy, precision, recall, F1-score, RMSE, and MAPE for the proposed and existing GLCM-ABC, PSO-ANN, IRB-CNN, FAGWO, and OCAM methods. The analysis shows that the performance of the proposed attained better effectiveness with attained accuracy by 98% and diminished for state-of-the-art techniques as 92%, 95%, 94%, 96.8%, as well as 96%, respectively. Compared to existing methods, the accuracy rate of the proposed method is maximised by 1.3%.

Dependable Automated Approach for Measuring the Retrograde Superior Ramus Screw Corridor in Pelvic Fracture Fixation.

Precise measurement of the intraosseous corridor within the superior pubic ramus is essential for the accurate percutaneous placement of a retrograde superior ramus screw (SRS). However, conventional manual measurement methods are often subjective, leading to variations in results among observers. Our goal was to develop an automated and dependable method for determining the retrograde SRS corridor. We developed an automated technique that utilized a computed tomography (CT) image-based search algorithm to identify the retrograde SRS corridor with the maximum diameter. We evaluated the reliability of this automated approach in comparison to a manual method using 17 pelves. Subsequently, we used both methods to measure the diameter, length, and orientation of the retrograde SRS corridor in 204 pelves in a Chinese population and assessed the intra- and interobserver agreement of each method by calculating the root-mean-square error (RMSE) and constructing Bland-Altman plots. We determined the screw applicability (percentages of hemipelves that could be treated with specific sizes of screws) for each method. Additionally, we investigated potential factors influencing the corridor, such as sex, age, height, and weight, through regression analysis. The intra- and interobserver intraclass correlation coefficients (ICCs) for the automated method (0.998 and 0.995) were higher than those for the manual approach (0.925 and 0.918) in the assessment of the corridor diameter. Furthermore, the diameter identified by the automated method was notably larger than the diameter measured with the manual method, with a mean difference and RMSE of 0.9 mm and 1.1 mm, respectively. The automated method revealed a significantly smaller corridor diameter in females than in males (an average of 7.5 and 10.4 mm, respectively). Moreover, use of the automated method allowed 80.6% of the females to be managed with a 4.5-mm screw while a 6.5-mm screw could be utilized in 19.4%, surpassing the capabilities of the manual method. Female sex had the most substantial impact on corridor diameter (β = -0.583). The automated method exhibited better reliability than the manual method in measuring the retrograde SRS corridor, and showed a larger corridor diameter for screw placement. Females had a significantly smaller corridor diameter than males. Given the intricate nature of the automated approach, which entails utilizing different software and interactive procedures, our current method is not readily applicable for traumatologists. We are working on developing integrated software with the goal of providing a more user-friendly solution for traumatologists in the near future. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Lightweight hashing with contrastive self-cross XLNet and combinational similarity matching for content-based video retrieval

ABSTRACT Text-to-video retrieval systems enable simple and natural video searches, but existing methods often employ single queries with noisy annotation and low-quality descriptions. A novel lightweight hashing with Contrastive self-cross XLNet and Combinational similarity matching is proposed to provide high-quality descriptions and clarity in annotation. Issues in existing text and video processing approaches, such as redundancy in keyword generation and extracting frames without considering optical flow estimation of keyframes, are solved using a novel Textual Labelling and Automated key frame segmentation. The 3D Cuboid Net with Lightweight Hashing is proposed, which selects features and converts them into hash codes using Lightweight LSS-Net based hashing, reducing memory utilization. Additionally, a novel Combinational Attentive Text-Video Retrieval is proposed, which eliminates multiple narrow extrema matching error surfaces and increases retrieval validity. The model efficiently solves video retrieval problems with high accuracy, recall, and sensitivity. This novel approach addresses the limitations of existing text-to-video retrieval methods and improves the overall quality of the retrieval process.

Content based COVID-19 image retrieval system using local histogram equalization and deep convolutional neural network

Doctors play a critical role in interpreting medical images as part of their core responsibilities. They need to find comparable examples that can assist in making informed decisions, especially when encountering ambiguous visuals. Traditionally, Systems such as content-based medical image retrieval (CBMIR) have been used for this. The proposed method employs a novel technique, local histogram equalization (LHE) for preprocessing, transfer learning-based convolutional neural network to extract the representative features with Manhattan and Euclidean distance metrics to assess how similar the query image and database image are to one another. This model is trained on a standard dataset namely Chest X-Ray images. Top-k, Precision and Recall measure is employed to assess system performance. From the results, the suggested enhanced convolutional neural network (CNN) model demonstrates significantly superior performance in the top 10 retrieval rates of 97.13% for coronavirus disease 2019 (COVID-19), 96.84% for normal, 82.63% for pneumonia-bacterial, and 81.72% for pneumonia-viral and precision@recall10 of 93.14% for COVID-19, 91.88% for normal, 77.84% for pneumonia-bacterial and 74.71% for pneumonia-viral.