Image Retrieval Framework Research Articles

Rapid Relevance Feedback Strategy Based on Distributed CBIR System

This article describes the capability of online data storage which has been enhanced by the emergence of cloud datacenter development. Distributed Hash Table (DHT) based image retrieval system using locality sensitive hash (LSH) has provided an efficient way to set up distributed Content Based Image Retrieval (CBIR) frameworks. However, with the fixed LSH function adopted, LSH and other codebook-based distributed retrieval systems are facing the problem of flexibility, and also are difficult to satisfy the user's demand. In this article, LRFMIR is proposed to introduce semantic search into DHT based CBIR system. LRFMIR is established on a DHT based network, where a flexible result truncating strategy is employed to fuse provided results by using multiple features measurements. Experiments show that LRFMIR provides a higher accuracy and recall rate than single feature employed retrieval systems, and possesses good load balancing and query efficiency performance.

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements, and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets: Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset, and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models.

Novel content based medical image retrieval based on BoVW classification method

Amount of digital image is increasing exponentially nowadays. Storage requirements for these images increase from Gigabytes to Pet bytes. Searching and retrieving the relevant images from such larger volume of images datasets based on its contents plays dynamic role in various applications for computer vision. There are many common retrieval systems are there to provide solutions for this problem. Times taken to retrieve the images are high and accuracy of retrieved images is less in the existing systems. The limitation of Hadoop maps reduce is the lack of performing real time tasks efficiently. A proficient content-based image retrieval framework based on Spark Map Reduce with bag of visual word is to be proposed to perform with high accuracy for big data. The Apache spark programming can be utilized to productively recover pictures with less retrieval time and retrieve the accurate images from the big database that resemble the query image. The proposed system obtained an accuracy of 97.7% and it is better than the existing classification method for Retrieve the image from the large database.

DCR‐Net: Dilated convolutional residual network for fashion image retrieval

AbstractFashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods.

Image reconstruction using a refined Res-UNet model for medical image retrieval

<p>Measurement of medical data and extraction of medical images with various sensory systems have become a crucial part of diagnosing and treating various diseases. In this contest measurement technology plays a key role in diagnosis. Medical experts usually use previous case studies to identify and deal with the current medical condition. In this context, an expert required to explore the large medical database to search relevant images for the required analysis. Searching such large database and retrieving an image efficiently becomes very tedious task. Therefore, this paper proposed a measurement-based image reconstruction using the refined Res-UNet Framework for Medical Image Retrieval (MIR) for managing such crucial tasks and reduce complexities. The proposed two stage framework consists of an image reconstruction using Res-UNet and index similarity matching of query image with history images. Res-UNet is a vanilla combination of ResNet50 as an encoder that gives latent information of input image, and the decoder from UNet reconstructs the image using latent information. Further, those latent features match similar image features and retrieve indexed images from the medical image database. The efficacy of proposed method was confirmed on benchmark medical image databases such as ILD and VIA/ELCAP-CT for MIR. The proposed framework outperforms the existing methods in the task of MIR.</p>

Performance comparison of image enhancers with and without deep learning.

Image enhancement is a computational procedure to improve visibility of details and content of an input image. Several image enhancement algorithms have been developed thus far, from traditional methods that process a single image based on physical models of image acquisition and formation to recent deep learning techniques, where enhancement models are learned from data. Here, we empirically compare a set of traditional and deep learning enhancers, which we selected as representing different methodologies for the improvement of poorly illuminated images. Our experiments are conducted on public data and show that, although all the considered enhancers improve the visibility of the image content and details, the deep-learning-based methods generally produce less noisy images than the traditional ones. This last outcome has to be carefully considered when enhancers are used as preprocessing for algorithms that are sensitive to noise. As a case study, and with the purpose to promote more aware usage of these two groups of enhancers in computer vision applications, we discuss the impact of image enhancement in the framework of image retrieval performed through two popular algorithms, i.e., SIFT and ORB, implementing different image descriptions and having different sensitivities to noise.

Design of deep convolution feature extraction for multimedia information retrieval

PurposeThe analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part. The representation of digital images interpreted by humans is subjective in nature and complex. Hence, searching for relevant images from the archives is difficult. Thus, electronic image analysis strategies have become effective tools in the process of image interpretation.Design/methodology/approachThe traditional approach used is text-based, i.e. searching images using textual annotations. It consumes time in the manual process of annotating images and is difficult to reduce the dependency in textual annotations if the archive consists of large number of samples. Therefore, content-based image retrieval (CBIR) is adopted in which the high-level visuals of images are represented in terms of feature vectors, which contain numerical values. It is a commonly used approach to understand the content of query images in retrieving relevant images. Still, the performance is less than optimal due to the presence of semantic gap among the image content representation and human visual understanding perspective because of the image content photometric, geometric variations and occlusions in search environments.FindingsThe authors proposed an image retrieval framework to generate semantic response through the feature extraction with convolution network and optimization of extracted features using adaptive moment estimation algorithm towards enhancing the retrieval performance.Originality/valueThe proposed framework is tested on Corel-1k and ImageNet datasets resulted in an accuracy of 98 and 96%, respectively, compared to the state-of-the-art approaches.

FCNet: A feature context network based on ensemble framework for image retrieval

In order to train an end-to-end model for better performance in distinguishing superclasses and make the model more sensitive to inter-class differences, the authors propose a Feature Context Network (FCNet), a novel ensemble framework for image retrieval based on clustering (EFC) and metric learning. In the authors' approach, the EFC framework consists of one common feature extractor and multiple learning branches. This tree-like structure not only promotes the stability of the training process but also keeps the diversity by using multiple branches. Each branch is learnt by an independent random re-labelling operation that distributes original classes into superclasses. In such a way, the framework thus can accelerate the training process. To make the EFC framework focus on the different attributes across the classes, the authors utilize k-means clustering to divide the training set into subsets and train the model sequentially with those subsets. Meanwhile, to reduce the impact of over-fitting problems, the authors design a branch weight structure during training. Further, FCNet fusions different scales of the neighbourhood information and consequently makes the feature map learn richer information with the different receptive fields. Simultaneously, it can reduce the layer of the network and the number of model parameters. Extensive experiments demonstrate that this approach outperforms the tested representative methods on CARS-196, CUB-200-2011 datasets.

An efficient framework for zero-shot sketch-based image retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) has recently attracted the attention of the computer vision community due to its real-world applications, and the more realistic and challenging setting that it presents over SBIR. ZS-SBIR inherits the main challenges of multiple computer vision problems including content-based Image Retrieval (CBIR), zero-shot learning and domain adaptation. The majority of previous studies using deep neural networks have achieved improved results by either projecting sketch and images into a common low-dimensional space, or transferring knowledge from seen to unseen classes. However, those approaches are trained with complex frameworks composed of multiple deep convolutional neural networks (CNNs) and are dependent on category-level word labels. This increases the requirements for training resources and datasets. In comparison, we propose a simple and efficient framework that does not require high computational training resources, and learns the semantic embedding space from a vision model rather than a language model, as is done by related studies. Furthermore, at training and inference stages our method only uses a single CNN. In this work, a pre-trained ImageNet CNN (i.e., ResNet50) is fine-tuned with three proposed learning objects: domain-balanced quadruplet loss, semantic classification loss, and semantic knowledge preservation loss. The domain-balanced quadruplet and semantic classification losses are introduced to learn discriminative, semantic and domain invariant features by considering ZS-SBIR as an object detection and verification problem. To preserve semantic knowledge learned with ImageNet and exploit it for unseen categories, the semantic knowledge preservation loss is proposed. To reduce computational cost and increase the accuracy of the semantic knowledge distillation process, ground-truth semantic knowledge is prepared in a class-oriented fashion prior to training. Extensive experiments are conducted on three challenging ZS-SBIR datasets: Sketchy Extended, TU-Berlin Extended and QuickDraw Extended. The proposed method achieves state-of-the-art results, and outperforms the majority of related works by a substantial margin.

NOVEL CBIR System Using Spark MAP-Reduce with a Firefly Macqueen’s K-Means Clustering Algorithm

Nowadays content-based image retrieval (CBIR) has been an active research area, demonstrating a feasible solution for recovering similar images from an image mine. The amount of digital images increases exponentially day by. Storage requirements for these images may increase from Gigabytes to Petabytes. Searching and retrieving the relevant images from such a large volume of image datasets, based on their contents, play a dynamic role in various applications of computer vision. The time taken to retrieve the images is more, and the accuracy of retrieved images is less in the existing systems. The limitation of Hadoop map-reduce is the lack of performing real-time tasks efficiently. A proficient content-based image retrieval framework based on Spark Map-Reduce with a Firefly MacQueen’s k-means clustering (FMKC) algorithm and Bag of visual word (BoVW) is proposed to achieve high accuracy for big data. The Apache spark programming can be used to productively recover pictures with less retrieval time and retrieve the accurate images from the big database that resembles the query image. The experimental results demonstrate that the method proposed in our work outperforms the state-of-the-art methods in terms of accuracy of the retrieved images and average retrieval time. The proposed system is 93% accurate, and it is easier to retrieve the images from the large database.

Adversarial image reconstruction learning framework for medical image retrieval

Adversarial image reconstruction learning framework for medical image retrieval

Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval

Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured, along with their region-wise features in their region adjacency graphs. Also, the attention mechanism has often been applied to the nodes to highlight the essential features in each node. In this regard, a significant amount of high-frequency information is missed since each image segment is effectively summarized within a single node. To account for this and increase the learning capacity, we propose to attend over the edge/adjacency matrix to highlight the interactions among meaningful regions that contribute to supervised learning from images. We exploit this novel edge attention mechanism together with node attention to highlight essential image context by allowing more importance to the meaningful neighboring regions that highlight a relevant node. We implement the proposed context-attended GCN framework for image retrieval on the benchmarked UC-Merced and the PatternNet datasets. We observe a notable improvement in the results compared to the state of the art.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c- Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

A Study on Design and Development of Framework for Content-based Image Retrieval

CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.

Attention-based end-to-end CNN framework for content-based X-ray image retrieval

Attention-based end-to-end CNN framework for content-based X-ray image retrieval

Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features

This paper proposed an efficient image retrieval framework by feature-fusion of high-level features from the improved version of DarkNet-53, named GroupNormalized-Inception-Darknet-53 (GN-Inception-Darknet-53), and handcraft features extracted from both RGB and HSI color models. To extract the more detailed features of an image, we augmented one inception layer, which includes 1 × 1, 3 × 3, and 5 × 5 kernels in place of an existing 3 × 3 kernel. To make the normalization process of the proposed model less dependent on batch size, Group Normalization (GN) layer is used instead of Batch Normalization (BN). A modified version of dot-diffused block truncation coding (DDBTC) is used to extract handcraft features in RGB color space. For HSI color space, interchannel voting between hue, saturation, and intensity components is used as color feature. To extract shape features histogram of orientated gradient (HOG) is applied on RGB color space. To evaluate the efficiency of our proposed method, Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are calculated for Corel-1 K, Corel-5 K, Corel-10 K, VisTex, Stex & Color Brodatz datasets. In all datasets, the proposed method shows the best results for all the instances with a minimum average improvement of 7.02%.

Design and Development of a Content-Based Image Retrieval (CBIR) System for Computing Similarity.

The shared and stored mixed media information is growing, and looking for or retrieving a significant image from a chronicle is a challenging exploration issue. Any image retrieval model's primary objective is to hunt for and mastermind photos that have a visual semantic association with the user's query. The bulk of web indexes on the Internet fetch photos using content-based algorithms that require subtitles as additional information. The user submits a query by inputting some text or keywords that match the file's keywords. )e yield is generated based on keyword matching, and this cycle can obtain insignificant photos. The distinction between human visual understanding and manual naming/commenting is the fundamental reason for producing the irrelevant yield. Any image retrieval framework must meet the fundamental criterion of searching for and sorting comparable photos from the archive with as little human interaction as possible. As implied by the writing, the choice of aesthetic characteristics for any framework is determined by the end user's requirements. Discriminative feature representation is another fundamental requirement for any image retrieval framework. To make the feature more robust and unique in terms of depiction fusion of low-level visual features, a large computational cost is required to obtain more dependable results. Regardless of the case, an ill-advised feature selection can degrade the performance of an image retrieval model. Contrary to conventional ideabased approaches, content-based picture retrieval is incompatible with them. "Content-based" refers to the fact that the hunt evaluates the image's contents rather than its metadata, such as keywords, labels, or depictions associated with the image.

Image retrieval of MRI brain tumour images based on SVM and FCM approaches

Abstract Objectives The key test in Content-Based Medical Image Retrieval (CBMIR) frameworks for MRI (Magnetic Resonance Imaging) pictures is the semantic hole between the low-level visual data caught by the MRI machine and the elevated level data seen by the human evaluator. Methods The conventional component extraction strategies centre just on low-level or significant level highlights and utilize some handmade highlights to diminish this hole. It is important to plan an element extraction structure to diminish this hole without utilizing handmade highlights by encoding/consolidating low-level and elevated level highlights. The Fleecy gathering is another packing technique, which is applied in plan depiction here and SVM (Support Vector Machine) is applied. Remembering the predefinition of bunching amount and enlistment cross-section is until now a significant theme, a new predefinition advance is extended in this paper, in like manner, and another CBMIR procedure is suggested and endorsed. It is essential to design a part extraction framework to diminish this opening without using painstakingly gathered features by encoding/joining low-level and critical level features. Results SVM and FCM (Fuzzy C Means) are applied to the power structures. Consequently, the incorporate vector contains all the objectives of the image. Recuperation of the image relies upon the detachment among request and database pictures called closeness measure. Conclusions Tests are performed on the 200 Image Database. Finally, exploratory results are evaluated by the audit and precision.

Color texture image retrieval based on Copula multivariate modeling in the Shearlet domain

Due to the increasing number of images on the Internet, image retrieval framework is needed for image-based search. Shearlet transform is a sparse, multiscale, and multidimensional representation used to extract anisotropic features in image processing applications such as image fusion and image denoising. In this paper, we proposed a color texture image retrieval framework based on Shearlet domain modeling using Copula multivariate model. In the proposed framework, Gaussian Copula is used to model the dependencies between different sub-bands of the Non-Subsample Shearlet Transform (NSST) and non-Gaussian models are used for marginal modeling of the coefficients. Six different schemes are proposed for modeling NSST coefficients based on the four types of neighboring defined; moreover, Kullback–Leibler Divergence(KLD) close form is calculated in different situations for the two Gaussian Copula and non-Gaussian functions in order to investigate the similarities in the proposed retrieval framework. The Jeffery divergence (JD) criterion, which is a symmetrical version of KLD, is used for investigating similarities in the proposed framework. We have implemented our experiments on four texture image retrieval benchmark datasets, the results of which show the superiority of the proposed framework over the existing state-of-the-art methods. In addition, the retrieval time of the proposed framework is also analyzed in the two steps of feature extraction and similarity matching, which also shows that the proposed framework enjoys an appropriate retrieval time.