Content-based Image Retrieval Technology Research Articles

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

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

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

Background/Objectives: The MRI has proven to be extremely effective in detecting tumors, with millions of images created each day throughout the world. To find similar images from a vast collection, Content-Based Tumor Image Retrieval (CBTIR) technology has been used to analysis the medical image. In the traditional retrieval methods, retrieving a similar image from the large database is crucial task. To overcome this issue we developed deep learning based retrieval method. Methods: This research offers a retrieval approach based on predefined ResNet models for quick and accurate image retrieval. We tested various prominent ResNet models with different distance similarity metrics, and the best option was determined by this work. Findings: After the various evaluation of ResNet models with varied distance measures on the CE-MRI data set, ResNet50 model applied with Hamming distance yields 99.33% of retrieval precision. Novelty: This work used predefined ResNet models with the combination of Distance similarity metrics to achieve more accurate results on medical image retrieval compared to the other conventional methods. Keywords: Content Based Image Retrieval, Tumor Retrieval, Hamming Distance, Euclidean Distance, Minkowski

An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity.

In the context of “double carbon”, as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH.

Meta-Hashing for Remote Sensing Image Retrieval

With the explosive growth of the volume and resolution of high-resolution remote-sensing (HRRS) images, the management of them becomes a challenging task. The traditional content-based remote-sensing image retrieval (CBRSIR) technologies cannot meet what we expect due to the large volume of image archives and complex contents within HRRS images. As a successful approximate nearest neighborhood (ANN) search technique, Hash learning has received wide attention, especially when deep convolutional neural networks (DCNNs) appear. Due to DCNNs&#x2019; strong capacity of feature learning, many DCNN-based hashing methods have been proposed and achieved good performance for large-scale CBRSIR tasks. Nevertheless, their limitation is that a large of labeled training samples should be collected for training the deep models. To overcome this limitation, this article, therefore, develops a new supervised hash learning method for the large-scale HRRS CBRSIR task based on meta-learning, which could achieve well-retrieval performance with a few labeled training samples. First, taking the characteristics of HRRS into account, we develop a self-adaptive convolution (SAP-Conv) block and design a hashing net based on the block. SAP-Conv can learn robust features from HRRS images by exploring their multiscale information. Second, to enhance the generalization of the hashing net under a few labeled training samples, the hash learning is formulated in a meta-way, and we name it meta-hashing. Meta-hashing can effectively preserve the similarities between support and query set, and the similarities between samples within support set by the developed loss function. To further improve the performance of meta-hashing, we expand it to a dynamic version named dynamic-meta-hashing, in which the numbers of support and query are changeable in the training phase. Experimental results counted on the three widely used HRRS datasets demonstrate our dynamic-meta-hashing and meta-hashing can achieve promising performance in large-scale HRRS CBRSIR tasks based on a few training samples. Our source codes are available at <uri>https://github.com/TangXu-Group/Meta-hashing</uri>.

A Review on Recognition of Plant Disease using Intelligent Image Retrieval Techniques

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/ diseases. Insecticides are not always effective because they can be toxic to some birds. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomatoes and potatoes. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated method used to diagnose diseases of tomato plants.

CBI + R: A Fusion Approach to Assist Dermatological Diagnoses

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI [Formula: see text] R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works.

The application of interactive methods under swarm computing and artificial intelligence in image retrieval and personalized analysis

The aim is to explore the interactive methods based on swarm computing and improve the image retrieval effects and personalized recommendation accuracy. The interactive methods based on swarm computing are explored. The mechanism of swarm intelligence (SI) algorithm is analyzed, in which the particle swarm optimization (PSO) algorithm and its improved algorithm are selected. The selected algorithms are combined with content-based image retrieval technology and applied to the image retrieval process, thereby realizing personalized analysis and recommendation based on users’ interests. Finally, the image retrieval behaviors of users are analyzed through simulation experiments, which verify the accuracy of the recommendation results. In the six sets of experiments, the image retrieval system based on the quantum behavior PSO (QPSO) has better performance compared to other PSO and SI evolution algorithms. The image retrieval accuracy of the proposed Bayesian personalized ranking (BPR) optimization algorithm (BPR-U2B) has significantly better performance compared to other recommendation algorithms. The QPSO algorithm is the best SI evolution algorithm for image retrieval. The BPR-U2B algorithm is combined with the collaborative filtering algorithm based on BPR. It optimizes the objective function to limit the ranking results of the BPR algorithm, which is beneficial to complete the image recommendations and improve the personalized recommendation effects for users.

Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features

With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retrieved, its retrieval efficiency still cannot meet people’s needs. In this paper, we propose an image retrieval algorithm based on the combination of color and shape features. The cumulative histogram method is used to calculate the color features of the image, and 7 Hu invariant moments are calculated as shape features. The color and shape features are combined with certain weights, and the Euclidean distance is used as the similarity measure. Finally, the image is retrieved, and the related experiments are passed. By comparing with related experiments, the algorithm effectively improves the accuracy of image retrieval.

Reversible cellular automata image encryption for similarity search

With the development of cloud computing, people prefer to outsource images to cloud service for saving storage and convenient retrieval. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which causes the content-based image retrieval technology on plaintext domain unusable. In order to achieve similarity search on encrypted domain, this paper proposes reversible cellular automata image encryption for similarity search. We firstly define the similarity search on encrypted images, and analyze its feasibility when implementing pixel-based deterministic encryption. According to this, we introduce a deterministic encryption based on reversible cellular automata (DERCA). Then we put forward a two-level-granularity image encryption which supports extracting color histogram from encrypted image for similarity search, based on DERCA. The pixels in an image are encrypted in pixel-granularity or pixel-set-granularity to solve utility-security dilemma. And block-based permutation method is used to improve the image security and retrieval accuracy. Experiments on Corel-1000 demonstrate that the proposed method not only offers high image security, but also yields superior retrieval accuracy on encrypted images compared with other image encryption algorithms for similarity search.

Review of Image Expression of Content-based Image Retrieval Technology

With the continuous development of science and technology, the popularity and use of projectors, cameras and other electronic products have led to an ever-expanding image source. Due to the enormous workload of text-based image retrieval (TBIR) annotation, content-based image retrieval (CBIR) was proposed to solve this problem. The CBIR has experienced the process featured by an integration of image digital, image semantic, and other multiple image features. CBIR, however, still has shortcomings in embodying the high-level semantics of the image. As the ability of autonomously learning image features, the defects of CBIR in image expression can be effectively solved by deep learning, which thus has become a hot topic in current image expression researches. On the basis of content-based image retrieval technology, some readings and analyses are made in the article for exploring the future development of image retrieval technology in image expression field.

Content-based image retrieval model based on cost sensitive learning

How to retrieve the desired images quickly and accurately from the large scale image database has become a hot topic in the field of multimedia research. Many content-based image retrieval (CBIR) technologies already exist, but they are not always satisfactory. In many applications, the CBIR model based on machine learning relies heavily on the distance metric between samples. Although the traditional distance metric methods are simple and convenient, it is not always appropriate for CBIR tasks. In this paper, a novel distance metric learning (DML) method based on cost sensitive learning (CSL) is studied, and then it is used in a large margin distribution learning machine (LDM) to replace the traditional kernel functions. The improved LDM also takes into account CSL, and which is called CS-DLDM. Finally, CS-DLDM model is applied to CBIR tasks for implementation classification. We compare the proposed CS-DLDM model with other classifiers based on CSL. The experimental results show that the proposed CS-DLDM model not only has satisfactory classification performance but also the lowest misclassification cost, can effectively avoid the class imbalance of sample.

The Application of Content-based Image Emotional Semantic Recognition and Classification in Clothing E-commerce

People dress more and more sensual, more and more people like to wear clothing in line with their emotional expression, and online shopping convenience makes a lot of people like to buy clothing online, and the current clothing ecommerce websites do not classify clothing with their emotional semantic which could bring a certain inconvenience to the online buyers. Content-based image recognition and retrieval technology takes technical support for the emotional semantic automatic identification and classification of clothing commodity in the clothing e-commerce site. All the issues had been studied in this article which took clothing fabrics as an example to illustrate. All the work wants to cause the industry's attention.

A study on the Image Retrieval Technology Based on Color Feature Extraction

The text-based image retrieval technology is sufficiently mature now, but it still fails to be accurate. It is urgent to further investigate into the content-based image retrieval technology which is quite new and widely applied to a variety of fields. As color is one of the fundamental features of image, the retrieval based on the color features of image can effectively improve the efficient. In this paper, we analyzed and studied the color-based image retrieval and verified the universality of CBIR system in application with nighttime license plate identification case. To sum up, CBIR has a promising future in application. With the future development, it is believed to have higher retrieval efficiency and similarity when meeting the demand of people for image retrieval so that the users can rapidly and accurately locate the image resources they want against a sea of information and better help can be provided for the image classification.

Image-matching technology applied to Fifteenth-century printed book illustration

This article examines how image-matching and content-based image retrieval technologies can be fruitfully applied to track the reuse and circulation of illustrations found in Fifteenth-century printed books. The possibility of tracking precisely and quickly the multiple occurrences of a single woodcut through one or different editions will help scholars to further explore how printed material was exchanged between or copied by different printers, as well as to analyse the working practice of a single printer, through a detailed reconstruction of his use of the illustrations in the composition of the printing sheets. Through the application of innovative computer science technologies, we learn more about the practical use of images, and therefore their cultural and social function, at the pivotal time of the spread of printing in the Western world.

Content-Based Image Retrieval Based on Shape Similarity Calculation

In the content-based image retrieval technology, the performance of retrieval system using only a single image feature is generally unsatisfactory, and therefore the image retrieval system using two or more image features is more often used. When there is the target deformation or the size variation, the performance of image retrieval system using only shape features is not satisfactory, too. To solve these problems, in this paper, the extraction of image salient region and a shape representation methods of describing the image content are proposed, then they are used with image texture and color features to implement image retrieval. Experimental results show that the proposed image retrieval system can provide very good retrieval performance.

Museum Cultural Relics Retrieval System Based on Salient Region

In the field of image retrieval, the problem of semantic gap exists in the current content-based image retrieval technology (low-level features can’t describe the high-level semantics accurately and comprehensively), and it is also one of the important problems. At the same time, the museum cultural relic image has its particularity. Search for different databases, many researchers have done a lot of work in image retrieval. We also established a rich cultural relic database and classified the cultural relics. Making use of the SIFT feature extraction method based on saliency and cultural relic image retrieval in experimental museum is improved. It also improves the current cultural relic retrieval performance.

Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.

EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing

The content-based image retrieval (CBIR) has been widely studied along with the increasing importance of images in our daily life. Compared with the text documents, images consume much more storage and thus are very suitable to be stored on the cloud servers. The outsourcing of CBIR to the cloud servers can be a very typical service in cloud computing. For the privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before being outsourced, which will cause the CBIR technologies in plaintext domain unusable. In this paper, we propose a scheme that supports CBIR over the encrypted images without revealing the sensitive information to the cloud server. Firstly, the feature vectors are extracted to represent the corresponding images. Then, the pre-filter tables are constructed with the locality-sensitive hashing to increase the search efficiency. Next, the feature vectors are protected by the secure k-nearest neighbor (kNN) algorithm. The security analysis and experiments show the security and efficiency of the proposed scheme.

A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing

With the increasing importance of images in people’s daily life, content-based image retrieval (CBIR) has been widely studied. Compared with text documents, images consume much more storage space. Hence, its maintenance is considered to be a typical example for cloud storage outsourcing. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which makes the CBIR technologies in plaintext domain to be unusable. In this paper, we propose a scheme that supports CBIR over encrypted images without leaking the sensitive information to the cloud server. First, feature vectors are extracted to represent the corresponding images. After that, the pre-filter tables are constructed by locality-sensitive hashing to increase search efficiency. Moreover, the feature vectors are protected by the secure kNN algorithm, and image pixels are encrypted by a standard stream cipher. In addition, considering the case that the authorized query users may illegally copy and distribute the retrieved images to someone unauthorized, we propose a watermark-based protocol to deter such illegal distributions. In our watermark-based protocol, a unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user. Hence, when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction. The security analysis and the experiments show the security and efficiency of the proposed scheme.

An Efficient Content based Image Retrieval: CBIR

Due to the exponential growth of image data there is a dire need for innovative tools which can easily manage, retrieve images and images from the large image database. The most common approach which is being used is Content-Based Image Retrieval (CBIR) system. CBIR is the popular image retrieval system by which reterived the targetted image can be retrieved by matching the features of the given image. The goal of this paper is to develop an image retrieval based on content properties such as shape, color, texture etc. usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process instead of the traditional keyword-based approach. The CBIR technology has been used in several applications such as fingerprint identification, biodiversity information systems, digital libraries, medicine and historical research among others. This paper aims to develop a new efficient tool for CBIR based on above mention parameters using MATLAB.