Image Retrieval System Research Articles

Integrated Color and Intensity MSF Scheme for Image Retrieval Systems

Markov Stationary Features (MSF) based on homogeneous Markov chain model, for content-based image analysis, is getting popularity nowadays. It not only considers the distribution of colors, just as the histogram method does, but also characterizes the spatial co-occurrence of histogram patterns. However, handling a large-scale database of images with a degree of heterogeneity, a simple MSF method is not sufficient to discriminate the images as one requires. In this paper, an Integrated Color and Intensity MSF (ICI-MSF) based on non-homogeneous Markov Chain is proposed to overcome this shortcoming. By incorporating spatial co-occurrence of image intensities with the spatial co-occurrence information of colors and exploiting time inhomogeneous Markov chain concept, it is possible to improve certain aspects of the existing methods. Without compromising effectiveness and robustness, the method proposed in this paper keeps the feature level simplicity. Widely recognized databases namely WANG1000 and Corel10800, are used to evaluate and compare the performance of the proposed algorithm with the existing methods. The experimental results justify the effectiveness of the proposed method.

In‐Memory Search for Highly Efficient Image Retrieval

Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)‐based in‐memory search, achieving software‐comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in‐memory search with a high‐precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor‐based CAM, and several similar images are fetched for further high‐precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50×) compared to the CPU and higher search accuracy than the fully in‐memory computing method, thus alleviating the efficiency bottleneck of CPU‐based image retrieval.

Content-Based Image Retrieval System using Color Moment and Bag of Visual Words with Local Binary Pattern

Researchers have recently focused their attention on Content-Based Image Retrieval (CBIR). It has emerged as one of the most fascinating areas in image processing and computer vision. With CBIR, the most comparable pictures that match the query image are pulled from an image database. As a result, it necessitates feature extraction (Local / Global) and similarity calculation. This paper uses a CBIR technique to determine the images that best match the image query by utilizing both global and local image features. A color moment is used for global features to describe the complete image. Local Binary Pattern (LBP) as a local feature, on the other hand, extracts interest points by building a Bag of Visual Words (BoVW). The distance between the query and database image features is computed using the Euclidean distance. Precision and recall are computed on the Corel-1K dataset to assess the retrieval performance.

Intelligent Editing System for Behind-The-Scenes Documentaries Based on CG Image Optimization Algorithm

On the basis of studying the network planning technology and the characteristics of CG project production, the relevant calculation methods of the network diagram are discussed, and the network diagram of the system is established according to the implementation of the current task. The type characteristics of behind-the-scenes documentaries are three aspects: fixed subject matter, unified mode, poor narrative, strong sense of participation, theme attachment, and mutual contrast, and summarizes the function of commercial marketing and publicity to satisfy the audience's peeping psychology. In this paper, content-based image retrieval the technology is applied to the resource search module of CGProject, and an image retrieval system ImgSearch is designed and implemented. The system has made some optimizations in image retrieval performance. Experiments show that the system has good practicability in CGProject.

(Retracted) Design of street art image retrieval system based on virtual simulation technology for the public health environment

Creating a good public health environment can improve the public’s environmental health literacy level. Therefore, we propose a street art image retrieval system to address the problems of artists’ single creation method and the exhaustion of creative inspiration in the process of creating street art images in a public health environment. The system can retrieve the relevant image categories and cultural backgrounds, according to the street art images to be drawn, which reduces the artists’ creation burden and helps the public to better appreciate and understand art. In the image classification module, a ResNet34 street art image classification network with a nonlocal attention mechanism is proposed by combining the characteristics of street art images. The experimental results show that the method can achieve accurate classification of art images and can accurately retrieve relevant images based on the input art images, helping artists to better create and improve the public health environment.

Pattern Based Image Retrieval System by Using Clustering Techniques

Nowadays, the image database is growing in enormous size with heterogeneous categories. Similarly, there is rising demands from users in various ways. The most demanding domains in society are health care, Agriculture, commerce, and security. Healthcare domain is concerned with diagnosing the disease. Security domain is involved in investigation of the Criminals. Commerce domain needs analysis to recognize the right product. Agriculture domain requires processing of disease affected fruit images. The PBIR system that combines evidence from multiple digital image domains can reduce those problems of existing image retrieval systems. The PBIR system is used to find uncertain parts of the image during augmentation steps. The result is identified with various parameters (e.g., accuracy, precision, distance, sum of distance), showing that the performance of the k-medoids pam algorithm better than the existing iterative clustering algorithms

Image segmentation techniques using python and deep learning

Computer vision is one of the most important technology emerging in the IT market. Computer vision is a way to provide a powerful ability to see and interpret the real-time objects to the computer just like us or possibly even better than us. An image segmentation is the key processes in machine vision. It is the method of separate a high quality picture into non-overlapping multiple regions to get more precise and significant result. This technique provide us much more granular and reliable information about images like shape, color, and texture, etc. Image segmentation makes a huge impression in Image compression or Object detection techniques, Content-based image retrieval, Video surveillance, Medical imaging, and Traffic control system, etc. It is efficient to process small segments of images one by one to gather information and renounce the regions without information than to process an entire image for particular information. So, image segmentation is help us to dividing image into small segments for advance processing. We can divide a digital image using properties of images such as pixel values, intensity value, texture, shape, etc. So, we can achieve image segmentation by segmenting the images based on these properties. There are many techniques through which one can achieve image segmentation. Here, we discussed multiple techniques apply to achieve image segmentation and compare their benefits and drawbacks followed by the conclusion.

Image Retrieval Using Convolutional Autoencoder, InfoGAN, and Vision Transformer Unsupervised Models

Query by Image Content (QBIC), subsequently known as Content-Based Image Retrieval (CBIR) systems, may offer a more advantageous solution in a variety of applications, including medical, meteorological, search by image, and others. Such systems primarily use similarity matching algorithms to compare image content to get their relevance from databases. They are essentially measuring the spatial distance between extracted visual features from a query image and their correspondence in the dataset. One of the most challenging query retrieval problems is Facial Sketched-Real Image Retrieval (FSRIR), which is content similarity matching based. These facial retrieving systems are employed in a variety of contexts, including criminal justice. The difficulties of retrieving such sorts come from the composition of the human face and its distinctive parts. In addition, the comparison between these images is made from two different domains. Besides, to our knowledge, there is a rare existence of large-scale facial datasets that can be used to evolve the performance of the retrieving system. The success of the retrieval process is governed by the method used to calculate similarity and the efficient representation of compared images. However, by effectively representing visual features, the main challenge-posing component of such approaches might be resolved. Hence, this paper has several contributions that fill the research gap in content-based similarity matching and retrieving as follows: 1) The first contribution is extending the Chinese University Face Sketch (CUFS) dataset by including augmented images, introducing to the community a novel dataset named Extended Sketched-Real Image Retrieval (ESRIR). The CUFS dataset has been extended from 100 images to include 53,000 facial sketches and 53,000 real facial images. 2) The paper’s second contribution is proposing three new algorithms for sketched-real image retrieving based on convolutional autoencoder, Infogan, and Vision Transformer unsupervised models for large datasets. 3) Furthermore, to meet the subjective demands of the users because of the prevalence of multiple query formats. The third contribution of this paper is to train and assess the proposed algorithms across two additional facial datasets of various image sorts. 4) Recently, the majority of people have preferred searching for brand logo images, but it may be tricky to separate certain brand logo features from their alternatives and even from other features in an image. Thus, the fourth contribution is to compare logo image retrieval performance based on visual features derived from each of the three suggested retrieving systems. 5) The paper also proposes cloud-based energy-saving and computational complexity approaches in large-scale datasets. Due to the ubiquity of touchscreen devices, users often make drawings based on their fantasies for certain object image searches. Thus, the proposed algorithms were tested and assessed on a tough dataset of doodle-scratched human artworks. They are also studied for a multi-category dataset to cover practically all possible image types and situations. The results are compared with the most recent algorithms found in the literature. The results show that the proposed approaches are outperforming the recent algorithms.

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

A Method of Colour-Histogram Matching for Nigerian Paper Currency Notes Classification.

In this paper a new algorithm for classification of three Nigerian paper currency notes, namely 200, 500, and 1000 Naira (N) denominations is presented. The work examines the effectiveness of using only colour histograms to differentiate between the classes or denominations of the three Nigerian paper currency notes. The bin-heights of the histograms of the HSI component images for the paper currencies are used as features while a rule-based classifier designed to take advantage of the changes or variations in the histogram patterns is used to classify the paper currencies into the right denomination class. The algorithm involves the utilization of a simple and effective comparison strategy as opposed to the existing, too-rigid metrics for histogram-comparison used by other authors for color indexing in content-based image retrieval systems. Over a testing data-set of 300 samples, the algorithm achieved an average classification accuracy of 98.66%, and classification accuracies of 100%, 99% and 97% for the N=200, N=500 and N=1000 denominations, respectively. The proposed algorithm does not require extensive preprocessing of the paper-currency images and as such is fast in implementation.

Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index. Finally, according to the additive homomorphic property of Paillier homomorphic encryption, a similarity measurement method suitable for computing in the retrieval system’s security is ensured by the encrypted domain. The experimental results, which were obtained on Web Image Database from the National University of Singapore (NUS-WIDE), Microsoft Common Objects in Context (MS COCO), and ImageNet data sets, demonstrate the system’s robust security and precise retrieval, the proposed scheme can achieve efficient image retrieval without revealing user privacy. The retrieval accuracy is improved by at least 37% compared to traditional hashing schemes. At the same time, the retrieval time is saved by at least 9.7% compared to the latest deep hashing schemes.

Generative Adversarial Network-based Visual Similarity Recommendation

The purpose of a content-based recommendation system is to rank a set of results by how closely they match the user’s query. These days, virtually every online marketplace has some kind of recommendation system in place to steer customers to more goods and services they might enjoy. In this piece, we’ll go through how we used Generative Adversarial Networks to build an image retrieval system for e-commerce sites, with a focus on locating the best possible shoe photos to go along with a given product image. The writers of this paper conducted the study presented here. We demonstrate the outcomes of our study of the proposed deep learning network’s performance on a benchmark dataset, and we compare the network’s results to those of state-of-the-art systems.

Computer-aided diagnosis through medical image retrieval in radiology

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability.

Efficient Content-Based Image Retrieval System with Two-Tier Hybrid Frameworks

Abstract The Content Based Image Retrieval (CBIR) system is a framework for finding images from huge datasets that are similar to a given image. The main component of CBIR system is the strategy for retrieval of images. There are many strategies available and most of these rely on single feature extraction. The single feature-based strategy may not be efficient for all types of images. Similarly, due to a larger set of data, image retrieval may become inefficient. Hence, this article proposes a system that comprises of two-stage retrieval with different features at every stage where the first stage will be coarse retrieval and the second will be fine retrieval. The proposed framework is validated on standard benchmark images and compared with existing frameworks. The results are recorded in graphical and numerical form, thus supporting the efficiency of the proposed system.

Building an efficient content based image retrieval system by changing the database structure

The <span>amount of interest in digital images is growing because of the advent of the World Wide Web. Many different industries are making use of digital images today, including medicine, design, journalism, education, and more. Images can be stored and retrieved using a variety of techniques and methods. Nevertheless, the majority of those engines are dependent on Meta data (key-words, descriptions, tags). Although efficient, costly, and able to target keywords and tags with each image, and these engines have their shortcomings. Though it may be possible to use a filtering system to give more accurate results, there are additional issues.</span>

Text based annotation and integrating visual features for CBIR system in Deep Learning

In content-based image retrieval system, the user’s query results are a set of images sorted according to the feature similarities between the images with respect to the query. In the real world, the images are main information source and represents features of objects like their color, shape and other attributes. From the various domains like fashion, crime prevention and medicine etc, we need some efficient image searching, browsing and retrieval tools. Although lots of work has been done for indexing and retrieval of images, but still there is no universally accepted feature extraction, retrieval and indexing technique available which is both accurate and fast. In this paper we have presented an algorithm which analyzes an image based on objects and RGB components and give similar images as the output. The images will be stored in the database with a certain tag that describes it. These tags can be used for text based image retrieval and helps in visual feature extraction. Most of the current search engines use tag based image retrieval which is time consuming and less accurate in user’s perspective. We have overcome these problems in proposed algorithm. The experimental results, showing the effectiveness of algorithms are presented.

Content-Based Image Retrieval for Painting Style with Convolutional Neural Network

With the availability of large paintings dataset on the online platform in recent years, it opens up a new research perspective and researcher started to investigate paintings in a new and different ways. While Convolutional Neural Networks (CNN) have been successfully applied in computer vision domains, several researchers started to investigate the possibility of utilizing CNN for artwork classification and retrieval system. Image retrieval has been one of the most difficult disciplines in digital image processing because it requires scanning a large database for images that are comparable to the query image. It is commonly known that retrieval performance is largely influenced by feature representations in trained algorithm and similarity measures. Deep Learning has recently advanced significantly, and deep features based on deep learning have been widely used because it has been demonstrated that the features have great generalisation. In this paper, a convolutional neural network (CNN) is utilised to classify painting’s style and extract deep and high-level features from the paintings. Next, were used as an image retrieval system to retrieve similar images in artwork style of content which is useful for search or recommendation systems in online art collection. Our experiments show that this strategy significantly improves the performance of content-based image retrieval for the style retrieval task of painting and beat the current state-of-art for painting style classification.

Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction

Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user’s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users’ expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN).

A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions

Computer-based medical image retrieval (CBMIR) system helps practitioners to enhance their diagnostic abilities, speeds up accurate diagnosis, and minimizes intra-and inter-observer variability. We investigate in-depth insights on content-based image retrieval systems over the past 20 years, to enable improvements with an imperative feature set to enhance the performance of the proposed CBMIR. After a comprehensive survey, we have made some inferences from the literature survey which is beneficial for researchers and medical practitioners. Existing studies, consider patch sizes of 32 only where 16 and 64 are ignored. We conduct experiments using 16, 32, and 64 patch sizes and two retrieval approaches i.e. simple distance-based retrieval (SDR) and customized query-based approach (CQA). SDR exhibits high inter-class ambiguity, in which some of the retrieved images are from different classes posing similar feature values, which negatively impacts the retrieval performance. To tackle this, a framework has been proposed based on CQA-CBMIR which utilizes wavelet-based Riesz features and improves the retrieval outcomes. The proposed framework is compared with the state-of-the-art (SOTA) methods and obtained f1-score and accuracy i.e. 86.5% and 86.8% respectively. Furthermore, this study illustrates some of the unresolved issues in the literature that demand future researchers' utmost attention to investigate new pathways in this discipline.

A Secure Medical Image Transmission System Based On 2D Logistic Map and Diffie-Hellman Key Exchange Mechanisms

With the tremendous growth of searchable visual media, the content-based medical image retrieval and computer-aided diagnosis systems have become popular in recent years to improve knowledge and provide facilities for radiologists. Medical images transferred throughout public networks demand a mechanism that guarantees image privacy, ownership and source of origin reliability, and image integrity verification. For this reason, secure image retrieval and diagnosis scheme have been given considerable interest due to users’ security concerns. This work proposed a secure framework based on a two-dimensional (2D) chaotic map with Diffie–Hellman key exchange protocols to ensure patient information privacy and security. Consequently, from a security and protection perspective, the objective is to provide a privacy procedure for medical image retrieval systems through image encryption technique combined with a secure key exchange procedure to minimize the possibility of secret key interception by an unauthorized person. Simulation results and security analysis show that the suggested technique could protect images with minimal time complexity and a high level of security while also resisting numerous attacks.