Image Retrieval System Research Articles

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

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.

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 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.

Ontology supported semantic based image retrieval

In this study, a two-stage approach for developing a Semantic-Based Image Retrieval system supported by Ontology is proposed. In the initial stage, the Object Detection process is employed to identify objects within the image. Subsequently, a predicate describing the relationship between these two objects is determined using the developed Bi-directional Recurrent Neural Network (Bi-RNN) model. In the second stage, relations defined in the form of <subject-predicate-object> are transformed into Ontologies and utilized to search for images that are semantically similar. In addressing the primary challenge of Semantic Gap within the Semantic-Based Image Retrieval approach, the proposed solution involves measuring the number of similar relationships between two images through the utilization of entropy. The Semantic Gap between two images was computed using the Joint Entropy method, leveraging the number of relationships (X) identified in the query image and the total number of relationships (Y) in the image with similar relationships obtained as a query result. The proposed approach exhibits characteristics of a novel method within this field, distinct from other similar methods employed in Semantic-Based Image Retrieval through the utilization of Ontologies. In the performance measurement of the developed model, 91% accuracy was obtained according to the Recall@100 (Top-5 accuracy) result.

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

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.

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.

CBIF-M: A content-based image filtering and retrieval scheme using multi-classifier filtering framework

Recently, the image retrieval process appears to be a challenging task to filter out a huge volume of objectionable images from retrieval. It is very easy for people of all ages to obtain such obscene images with just a few clicks on the internet, which could harm the adolescent. To prevent such serious social problems, this paper introduces a content-based image filtering (CBIF) framework in an image retrieval system. The proposed CBIF approach is split into two components. In the first component, the skin region of exposed human body parts is extracted based on a color segmentation method, and an efficient feature vector generation process is introduced based on frequent skin color pixels and scale-invariant structural features. A novel texture feature representation is presented in the retrieval and filtering process obtained by block-paired local binary pattern (BP-LBP). The final component introduces a novel image filtering framework for the retrieved images found from the first component based on a majority of voting method on three different machine learning classifiers backed by SVM, MLP, and CNN. The retrieval efficiency is analyzed with standard performance indicators such as precision, recall, and F-score on two large datasets: NPDI and NSFW.

DarwinGSE: Towards better image retrieval systems for intellectual property datasets.

A trademark's image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

A deep learning based interval type-2 fuzzy approach for image retrieval systems

Deep learning, that one of its key benefits is automated feature extraction, has become a principal solution for computer vision. This paper presents a Deep Type-2 Beta Fuzzy (DT2F) approach for Content-Based Image Retrieval (CBIR) systems. Firstly, the suggested architecture uses InceptionResNetv2 a pre-trained deep learning model on Image-Net data as a feature extractor. Secondly, the obtained feature space is fuzzified to handle the uncertainties associated with the extracted values of deep features. Thirdly, the reduction dimensionality step is efficiently applied using a Multi-Variational Auto-Encoder (MVAE) to reduce computational complexity and achieve better performance. Ultimately, we retrieve images using the nearest neighbors rule based on type-2 fuzzy similarity to having higher proximity sensitivity. Extensive experimentations were accomplished on various image-retrieving datasets of different scales the proposed system achieved an average precision of 97.15% exceeding other state-of-the-art methods over many systems on Corel datasets, which can open the door for several hybridization breakthroughs in the area of image retrieval.

Enhanced Plant Leaf Disease Identification by Integrating 2DCNN and Transfer Learning for a Content-Based Image Retrieval System

Enhanced Plant Leaf Disease Identification by Integrating 2DCNN and Transfer Learning for a Content-Based Image Retrieval System

Markov Chains for User Queries Mining: Image Retrieval Application using Internet

Abstract: We suggest a brand-new technique for automatically annotating, indexing, and retrieving photos based on annotations. We introduce the novel technique, which we term Markovian Semantic Indexing (MSI), within the framework of an image retrieval system in online mode. In the event that such a system exists, the queries entered by the users are utilised to build an Aggregate Markov Chain (AMC), which defines the significance between the terms the system sees. The photographs are automatically annotated based on the queries entered by the users. Next, depending on the annotation of each image and the keyword relevance recorded in the AMC, a stochastic distance between them is presented. The suggested distance is given geometric meanings, and its relationship to a grouping in the keyword space is examined. The optimality qualities of the suggested distance are demonstrated by the use of a novel Markovian state similarity measure, the mean first cross passage time (CPT). Images are represented as points in a vector space, and MSI is used to calculate how similar two images are. In Annotation-Based Image Retrieval (ABIR) tasks, it is demonstrated that the novel method outperforms Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in terms of Precision against Recall and has some theoretical advantages.

A fully automatic adjacent key-points localization framework for minimal repeated pattern detection in printed fabric images

The printed fabric images usually contain a large number of minimal repeated patterns (MRPs), which are of great value for the color separation, plate-making and other related applications in the textile industry. The key-points localization is a well established and effective manual method to detect the MRPs by finding a number of identical key-points. Recently, computer-aided automated key-points localization techniques have shown great potential and attracted considerable interest. Nevertheless, most of the available subtle and ad-hoc strategies are poorly generalized to complex situations due to a lack of systematic formulation. In this study, we endeavor to automate and theorize this manual detection method, aiming to enhance its accuracy, robustness, and efficiency. Specifically, we delineate related concepts and systematically formalize the minimal repeated pattern (MRP) detection problem, thereby establishing a theoretical groundwork for the manual method’s viability. Then, we present an effective and robust adjacent key-points localization (AKL) framework by automating manual detection method of MRP in printed fabric images. This framework is further developed into a comprehensive automated image retrieval system, complete with detailed implementation techniques. We also create a printed fabric dataset named PFI-10K containing approximately 10,000 images to test corresponding methods. The experimental results demonstrate the effectiveness and robustness of the proposed AKL framework.

A Theoretical Review: Modern Techniques & Trends for Feature Extraction in Applications of Image Retrieval Systems

A Theoretical Review: Modern Techniques & Trends for Feature Extraction in Applications of Image Retrieval Systems

Collaborative group: Composed image retrieval via consensus learning from noisy annotations

Composed image retrieval extends content-based image retrieval systems by enabling users to search using reference images and captions that describe their intention. Despite great progress in developing image-text compositors to extract discriminative visual-linguistic features, we identify a hitherto overlooked issue, triplet ambiguity, which impedes robust feature extraction. Triplet ambiguity refers to a type of semantic ambiguity that arises between the reference image, the relative caption, and the target image. It is mainly due to the limited representation of the annotated text, resulting in many noisy triplets where multiple visually dissimilar candidate images can be matched to an identical reference pair (i.e., a reference image + a relative caption). To address this challenge, we propose the Consensus Network (Css-Net), inspired by the psychological concept that groups outperform individuals. Css-Net comprises two core components: (1) a consensus module with four diverse compositors, each generating distinct image-text embeddings, fostering complementary feature extraction and mitigating dependence on any single, potentially biased compositor; (2) a Kullback–Leibler divergence loss that encourages learning of inter-compositor interactions to promote consensual outputs. During evaluation, the decisions of the four compositors are combined through a weighting scheme, enhancing overall agreement. On benchmark datasets, particularly FashionIQ, Css-Net demonstrates marked improvements. Notably, it achieves significant recall gains, with a 2.77% increase in R@10 and 6.67% boost in R@50, underscoring its competitiveness in addressing the fundamental limitations of existing methods.

Benchmarking Deep Learning-Based Image Retrieval of Oral Tumor Histology.

Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology. This study uses a large CBIR database constructed from 30 categories of oral tumors to compare deep learning methods as feature extractors. The highest average area under the receiver operating characteristic curve (AUC) was achieved by models trained on database images using self-supervised learning (SSL) methods (0.900 with SimCLR and 0.897 with TiCo). The generalizability of the models was validated using query images from the same cases taken with smartphones. When smartphone images were tested as queries, both models yielded the highest mean AUC (0.871 with SimCLR and 0.857 with TiCo). We ensured the retrieved image result would be easily observed by evaluating the top 10 mean accuracies and checking for an exact diagnostic category and its differential diagnostic categories. Training deep learning models with SSL methods using image data specific to the target site is beneficial for CBIR tasks in oral tumor histology to obtain histologically meaningful results and high performance. This result provides insight into the effective development of a CBIR system to help improve the accuracy and speed of histopathology diagnosis and advance oral tumor research in the future.

Advancements in Non-Linear Content-Based Image Retrieval (CBIR) Systems for Image Analysis

Advancements in Non-Linear Content-Based Image Retrieval (CBIR) Systems for Image Analysis

SEMANTIC SEGMENTATION AND CONTENT-BASED RETRIEVAL IN MULTIMEDIA IMAGE DATABASES

Brain tumors, particularly gliomas, pose a significant threat to global health, necessitating accurate and efficient diagnostic methods. Magnetic Resonance Imaging (MRI) serves as a crucial tool for diagnosing glioma grades, but interpretation is subject to variability, hindering treatment planning. Intra and inter-observer variability in radiological image interpretation impede effective therapeutic strategies for brain tumor patients. Accessing relevant images from vast medical databases for comparison and treatment planning is cumbersome and time-consuming. This paper proposes a Content-Based Medical Image Retrieval (CBIR) system utilizing Convolutional Neural Network (CNN)-based feature extraction, specifically employing the AlexNet architecture. The system employs KNN clustering for indexing the feature map database and implements Gain-based feature selection to reduce feature vector dimensionality. The proposed system underwent evaluation using BraTS 2018 and 2020 datasets with five-fold cross-validation. Achieving state-of-the-art performance, the system demonstrated a mean Average Precision of 98% and Precision of 97%, showcasing its efficacy in accurately retrieving similar pathological MRI brain images.