Content-based Image Retrieval Research Articles

Machine learning and similar image-based techniques based on Nash game theory

The use of computer vision techniques to address the task of image retrieval is known as a Content-Based Image Retrieval (CBIR) system. It is a system designed to locate and retrieve the appropriate digital image from a large database by utilizing a query image. Over the last few years, machine learning algorithms have achieved impressive results in image retrieval tasks due to their ability to learn from large amounts of diverse data and improve their accuracy in image recognition and retrieval. Our team has developed a CBIR system that is reinforced by two machine learning algorithms and employs multiple clustering and low-level image feature extraction, such as color, shape, and texture, to formulate a Nash game. Consequently, we are faced with a multicriteria optimization problem. To solve this problem, we have formulated a three-player static Nash game, where each player utilizes a different strategy (color descriptor, Zernike descriptor, and SFTA descriptor) based on their objective function. The Nash equilibrium is defined as the membership classes of the query image.

Balancing Privacy and Performance: Exploring Encryption and Quantization in Content-Based Image Retrieval Systems

Balancing Privacy and Performance: Exploring Encryption and Quantization in Content-Based Image Retrieval Systems

Searching for similar images using Nash game and machine learning

The storage of large amounts of digital data, as well as the processing of digital images, are currently expanding significantly across a range of application areas. As a result, effective management of big images databases is necessary, which calls for the employment of automated and cutting-edge indexing techniques. One method used for this is Content-Based Image Retrieval (CBIR), which tries to index and query the picture database using visual aspects of the image rather than its semantic features. In this article, we propose to explore a digital search engine for similar images, based on multiple image representations and clustering, improved by game theory and machine learning methods.

DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based Image Retrieval.

Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely A, C, T , and G , with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.

Content-based fabric image retrieval system by exploiting dictionary learning approach

Content-based fabric image retrieval system by exploiting dictionary learning approach

Content-Based Image and Video Retrieval Based on Hybrid Feature Extraction Techniques

Content-Based Image and Video Retrieval Based on Hybrid Feature Extraction Techniques

Fabric image retrieval based on decoupling of texture and color feature

Fabric image retrieval, a form of content based image retrieval, is a high value research with the potential to be applied in many fields, such as e-commerce and inventory management. However, this research hotspot is plagued by two major challenges, namely the high requirements for retrieval results and the peculiarities of fabric images. Unlike general image retrieval, fabric image retrieval systems have to pay more attention to texture and color features. To address these challenges, we propose a novel framework for fabric retrieval by using self-supervised and deep hashing techniques. The framework consists of two modules for feature learning and hashing learning. During the feature learning phase, the color and texture information in the image is decoupled under the drive of augmented based pretext tasks. In hashing learning, Bi-half layer is introduced to generate high-quality hash codes. The visualization results indicate that the proposed method performs well for the representation of fabric images. And the experimental results show that the proposed retrieval framework can achieve a good performance (best mAP 0.903) and outperforms other methods, including several deep hashing methods and our previous work.

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Archimedes Optimization Algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

Privacy-Preserving Image Retrieval Based on Additive Secret Sharing

The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With more cloud service providers, the collaborative secure image retrieval service provided by multiple cloud servers becomes possible. In this paper, inspired by additive secret sharing technology, we propose a series of additive secure computing protocols on numbers and matrices with better efficiency, and then show their application in PPCBIR. Specifically, we extract CNN features, decrease the dimension of features and build the index securely with the help of our protocols, which include the full process of image retrieval in the plaintext domain. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.

Using fuzzy similarity measure in content-based video retrieval based on image query

Using fuzzy similarity measure in content-based video retrieval based on image query

Research on the Integration and Innovation of Artificial Intelligence Technology in Modern Brand Design

Abstract In the context of the artificial intelligence era, the application of artificial intelligence technology in modern brand design is increasingly widespread and is profoundly changing the design creation in this field. The purpose of this paper is to propose an intelligent image acquisition and automated brand design technology that utilizes AI technology to assist with brand design. The image acquisition and retrieval process of the online CBIR system is established, the average precision mean is used as the evaluation index of image retrieval, and the BoF model is constructed to realize RootSIFT feature extraction and image acquisition. Meanwhile, MultiStyleLogo GAN, a multi-style generative adversarial network, is designed to intelligently automate random output and generate brand design solutions through input sketches and style semantic descriptions to improve designers’ design efficiency. This paper utilizes technology to redesign the brand of A green plum wine in a province in China. Except for the pattern and font indicators, the average value of the newly designed brand image in each evaluation index of aesthetic design is greater than 4. The average values of the image aesthetics, cultural connotation, and emotional resonance indicators in the evaluation of brand narrative reach 4.25, 4.24, and 4.15, respectively, which achieve a high consumer satisfaction level.

Secure Content Based Image Retrieval System Using Deep Learning

This paper investigates Content-Based Image Retrieval (CBIR) using an ensemble of three cutting-edge deep learning architectures: Xception, MobileNet, and Inception. This ensemble approach demonstrated exceptional retrieval accuracy, with Xception and Inception models achieving an accuracy of 92.375%, precision and recall of 93% and 92% respectively, and an F1-score of 92%. The MobileNet model also showed strong performance, with an accuracy of 87.125%, precision and recall of 88% and 87%, and an F1-score of 87%.Beyond mere retrieval accuracy, the study places a significant emphasis on the security of the image database. A dual-layer encryption method was employed, integrating visual cryptography with the Advanced Encryption Standard (AES) to ensure robust protection of sensitive data. This approach guarantees efficient image retrieval based on content while securing the data against potential breaches.The results underscore the efficiency of the ensemble model in balancing high retrieval accuracy with stringent security measures. This balance is particularly relevant for applications in digital libraries, historical research, fingerprint identification, and crime prevention. The paper’s findings advocate for the critical need to integrate strong security protocols in future CBIR systems, ensuring optimal performance without compromising data security.

An Efficient Video Frames Retrieval System Using Speeded Up Robust Features Based Bag of Visual Words

Most studies in content-based image retrieval (CBIR) systems use database images of multiple classes. There is a lack of an automatic video frame retrieval system based on the query image. Low-level features i.e., the shape and colors of most of the objects are almost the same e.g., the sun and an orange are both round and red in color. Features such as speeded up robust features (SURF) used in most of the content-based video retrieval (CBVR) & CBIR research work are non-invariant features which may affect the overall accuracy of the CBIR system. The use of a simple and weak classifier or matching technique may also affect the accuracy of the CBIR system on high scale. The unavailability of datasets for content-based video frames retrieval is also a research gap to be explored in this paper.

Highly compressed image representation for classification and content retrieval

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.

Sketch-based semantic retrieval of medical images

The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images. The key concept is feature decomposition of medical images, which allows the entire feature of a medical image to be decomposed into and reconstructed from normal and abnormal features. Building on this concept, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. The system integrates both types of input to construct a query vector and retrieves reference images. For evaluation, ten healthcare professionals participated in a user test using two datasets. Consequently, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for rare cases. Our SBMIR system provides on-demand, customizable medical image retrieval, thereby expanding the utility of medical image databases.

Jumping particle swarm optimization algorithm framework for content-based image retrieval system

Content-based image retrieval (CBIR) has been studied well in the last decades in numerous research fields such as medicine, journalism, and private life. Applications of CBIR have been widely employed in medical images due to their direct impact on human life. With continues growing of digital libraries, there is a need for an efficient method to retrieve images from large datasets. In this paper, a new method was developed for CBIR based on the jumping particle swarm optimization (JPSO) algorithm. The proposed algorithm represents a developed instant of particle swarm optimization (PSO). However, JPSO the approach does not consider the velocity components to guide particle movements in the problem space. Instead of relying on inertia and velocity, intermittently random jumps (moves) occur from one solution to another within the discrete search space. To test the performance of the proposed algorithm, three types of medical image databases were used in the experiment which are the endoscopy 100, dental 100, and 50 skull image databases. The results show that the proposed algorithm could achieve high accuracy in image extraction and retrieve the accurate image category compared with other research works.

AstroSer: Leveraging Deep Learning for Efficient Content-based Retrieval in Massive Solar-observation Images

Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the capabilities of the deep learning models ResNet50 and VGG16 and have used it to extract key features of solar activity and solar environmental characteristics from observed images. This model enables efficient image matching and allows for content-based image retrieval (CBIR). Experimental results demonstrate that the model can achieve up to 98% similarity during CBIR while exhibiting adaptability and scalability. Our work has implications for astronomical research, data management, and education, and it can contribute to optimizing the utilization of astronomical image data. It also serves as a useful example of the application of deep learning technology in the field of astronomy.

Content-based image retrieval based on corel dataset using deep learning

A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.

Generic Graphical User Interface for CBIR Framework

AbstractContent-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.

Effect of Genetic Algorithm as a Feature Selection for Image Classification

Analysis of image content is important in the classification of images, identification, retrieval, and recognition processes. The medical image datasets for content-based medical image retrieval ( are large datasets that are limited by high computational costs and poor performance. The aim of the proposed method is to enhance this image retrieval and classification by using a genetic algorithm (GA) to choose the reduced features and dimensionality. This process was created in three stages. In the first stage, two algorithms are applied to extract the important features; the first algorithm is the Contrast Enhancement method and the second is a Discrete Cosine Transform algorithm. In the next stage, we used datasets of the medical images using GA-based feature selection to find feature vectors. Images from the datasets and images from the query are recognized using a correlation coefficient. The third stage of the proposed method used a diverse density algorithm feedback technique to enhance the performance of the . Images of breast cancer, brain cancer, lung cancer, thyroid cancer, etc., may be retrieved using the suggested procedure. By using a feature selection algorithm based on GA to determine the best subset of features, the challenge of system dimensionality is reduced. The suggested method has greater accuracy in precision, recall, and F-score than the other techniques.