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content-based image retrieval for Multi-Class Volumetric Radiology images: A Benchmark Study

IEEE ACCESS 2025年 13卷 68066-68083页

作者： Jush, Farnaz Khun Vogler, Steffen Truong, Tuan Lenga, Matthias Bayer AG Radiol Dept D-13353 Berlin Germany

With the growing number of images generated daily in radiological practices and the digitization of historical studies, we face large databases where metadata can be incomplete or incorrect. content-based image retrieval (CBIR) can help to manage these datasets by efficiently locating and retrieving relevant information by only using image data as a query. Recent studies have shown the potential use of pre-trained vision embeddings for CBIR in the context of radiology image retrieval for specific tasks like single-organ retrieval or distinguishing between normal and abnormal tissues. However, a comprehensive benchmark for the retrieval of 3D volumetric medical images is still lacking. In this study, we establish a benchmark for multi-class volumetric retrieval for different retrieval modes. The retrieval modes are defined based on end-point use-cases, which include Full-Scan retrieval, Targeted-Region retrieval, and Precise-Localization retrieval. We utilize the TotalSegmentator dataset, comprising 104 detailed anatomical structures, to evaluate the performance of embeddings derived from pre-trained models across the proposed retrieval modes. We show the potential of CBIR for organ retrieval by achieving a retrieval recall of 1.0 for diverse anatomical regions with a wide size range. The methodology presented in this paper utilizes publicly available image data and embedding models without requiring any task-specific fine-tuning. This benchmark aims to enable further development and allow for a more consistent evaluation of CBIR approaches in the context of 3D medical imaging.

关键词： Feature extraction image retrieval Databases Benchmark testing Three-dimensional displays Radiology Indexing Training Residual neural networks Visualization content-based image retrieval medical imaging pretrained embeddings

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A new texture descriptor based on hexagonal local binary pattern for content-based image retrieval

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DIGITAL SIGNAL PROCESSING 2025年 161卷

作者： Fadaei, Sadegh Azadimotlagh, Mehdi Rashno, Armin Beheshti, Amin Univ Yasuj Fac Engn Dept Elect Engn Yasuj Iran Persian Gulf Univ Jam Fac Engn Dept Comp Engn Bushehr Iran Lorestan Univ Fac Engn Dept Comp Engn Khorramabad Iran Macquarie Univ Sch Comp Sydney Australia

S R C Texture features play a vital role in content-based image retrieval (CBIR) applications. Most texture extraction methods have a low accuracy and high feature vector length. This paper presents a novel hexagonal local binary pattern (HLBP) to extract more informative and compact features from images. To have robust patterns against rotation, rotation invariant hexagonal patterns are presented using cyclic set theory. Texture feature vector is extracted from hexagonal images based on proposed patterns and used in CBIR application. To evaluate proposed method, experiments are performed in five datasets Corel-1k, Brodatz, VisTex, Corel-10k, and STex. The proposed HLBP method outperforms square local binary pattern (SLBP) in images with noise in the terms of precision. The feature vector length of the proposed method is 64, which is much shorter than those in competitive methods and leads to high speed in retrieval phase. The best performance of the proposed method is revealed in texture datasets which achieved the highest precision among all competitive methods.

关键词： Local binary pattern Square lattice Hexagonal lattice content-based image retrieval

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Development of improved heuristic-assisted deep learning for content-based image retrieval systems using multi-similarity measures

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IMAGING SCIENCE JOURNAL 2025年

作者： Khandelwal, Sarika Vyas, Ruchi Singh, Neha Vyawahare, Harsha G H Raisoni Coll Engn Dept Comp Sci & Engn Nagpur India Geetanjali Inst Tech Studies Udaipur Dept Comp Sci & Engn Udaipur India Galgotias Univ Sch Comp Sci & Engn Gautam Buddha Nagar Uttar Pradesh India St Gadge Baba Amarvati Univ Sipna Coll Engn & Technol Dept Comp Sci & Engn Amaravati India

In this research work, a novel content-based image retrieval (CBIR) model with the utilization of deep learning techniques and adaptive concepts is executed. Initially, the required images are collected from benchmark data sources. The gathered images are fed to the feature extraction process whereas the Adaptive Residual Attention Network (A-RAN) is analyzed to obtain the necessary deep features. Here, the parameters in the RAN are tuned by the Improved Black Widow Optimization Algorithm (IBWOA). The extracted features are considered for analyzing the multi-similarity measures. Using the similarity score, the images are retrieved from the database for the provided query. By comparing conventional techniques, the precision of the developed model attains 22.05%, 27.69%, 38.7%, and 20.2% better performance than CNN, CapsNet, DBN, and BMWDLNN, respectively. It helps the recommended framework to provide better performance by providing a visual representation of effectively retrieving the relevant content.

关键词： content-based image retrieval adaptive residual attention network improved black widow optimization algorithm multi-similarity function relief score maximization deep feature extraction parameter optimization image retrieval with high similarity score

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Deterministic and Probabilistic Certified Defenses for content-based image retrieval

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2025年第1期E108D卷 92-109页

作者： Kakizaki, Kazuya Fukuchi, Kazuto Sakuma, Jun NEC Corp Ltd Kawasaki 2118666 Japan Univ Tsukuba Tsukuba 3058573 Japan RIKEN AIP Tokyo 1030027 Japan Inst Sci Tokyo Tokyo 1528550 Japan

This paper develops certified defenses for deep neural netexamples (AXs). Previous works put their effort into certified defense for classification to improve certified robustness, which guarantees that no AX to cause misclassification exists around the sample. Such certified defense, however, could not be applied to CBIR directly because the goals of adversarial attack against classification and CBIR are completely different. To develop the certified defense for CBIR, we first define the new certified robustness of CBIR, which guarantees that no AX that changes the ranking results of CBIR exists around the input images. Then, we propose computationally tractable verification algorithms that verify whether a given feature extraction DNN satisfies the certified robustness of CBIR at given input images. Our proposed verification algorithms are achieved by evaluating the upper and lower bounds of distances between feature representations of perturbed and non-perturbed images in deterministic and probabilistic manners. Finally, we propose robust training methods to obtain feature extraction DNNs that increase the number of inputs that satisfy the certified robustness of CBIR by tightening the upper and lower bounds. We experimentally show that our proposed certified defenses can guarantee robustness deterministically and probabilistically on various datasets.

关键词： adversarial example certified defense content-based image retrieval

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content-based image retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

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IEEE ACCESS 2023年 11卷 95410-95431页

作者： Srivastava, Divya Singh, Shashank Sheshar Rajitha, B. Verma, Madhushi Kaur, Manjit Lee, Heung-No Bennett Univ Sch Comp Sci Engn & Technol SCSET Greater Noida 201310 India Thapar Inst Engn & Technol Dept Comp Sci & Engn CSED Patiala 147004 India MNNIT Allahabad Dept Comp Sci & Engn CSED Prayagraj 211004 India SR Univ Sch Comp Sci & Artificial Intelligence Warangal 506371 India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

In the era of massive data production through the internet and social media, the volume of images generated is immense. Storing and retrieving relevant images efficiently pose significant challenges. content-based image retrieval (CBIR) has emerged as a prevalent method for retrieving relevant images based on query images from large image collections. CBIR relies on three fundamental elements: the selection, extraction, and representation of features. This paper delves into a comprehensive survey of these crucial aspects. This paper begins by investigating the significance and wide-ranging applications of CBIR. It subsequently delves into an intricate analysis of feature selection, encompassing attributes such as color, texture, shape, and descriptors. Following this, the paper navigates through sections dedicated to feature extraction techniques and their subsequent representation. Furthermore, this paper includes an assessment of recent research articles and the methodologies they employ within the realm of CBIR. Significantly, CBIR has witnessed a notable expansion to incorporate deep learning techniques in recent times. The survey presents an overview of these recent methods and their integration into CBIR frameworks. This paper concludes by offering an extensive outline of 215 articles, encompassing a wide range of analyses conducted within the field of CBIR. Finally, this paper also outlines potential research directions for the future. It sheds light on areas where CBIR can continue to evolve and enhance its capabilities.

关键词： content-based image retrieval text-based image retrieval deep learning image processing image retrieval color feature texture feature shape feature key point descriptors speed up robust feature

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content-based image retrieval by combining convolutional neural networks and sparse representation

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第15期78卷 20895-20912页

作者： Sezavar, Amir Farsi, Hassan Mohamadzadeh, Sajad Univ Birjand Dept Elect & Comp Engn Birjand Iran

As stored data and images on memory disks increase, image retrieval has a necessary task on image processing. Although lots of researches have been reported for this task so far, semantic gap between low level features of images and human concept is still an important challenge on content-based image retrieval. For this task, a robust method is proposed by a combination of convolutional neural network and sparse representation, in which deep features are extracted by using CNN and sparse representation to increase retrieval speed and accuracy. The proposed method has been tested on three common databases on image retrieval, named Corel, ALOI and MPEG7. By computing metrics such as P(0.5), P(1) and ANMRR, experimental results show that the proposed method has achieved higher accuracy and better speed compared to state-of-the-art methods.

关键词： content-based image retrieval Deep learning Convolutional neural networks Sparse representation

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content-based image retrieval OF CULTURAL HERITAGE SYMBOLS BY INTERACTION OF VISUAL PERSPECTIVES

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2011年第5期25卷 643-673页

作者： Kwan, Paul W. Kameyama, Keisuke Gao, Junbin Toraichi, Kazuo Univ New England Sch Sci & Technol Armidale NSW 2351 Australia Univ Tsukuba Grad Sch SIE Dept Comp Sci Tsukuba Ibaraki 3058573 Japan Charles Sturt Univ Sch Comp & Math Bathurst NSW 2795 Australia Univ Tsukuba Tsukuba Ind Liaison & Cooperat Res Ctr Fluency Labs Inc Tsukuba Ibaraki 2058573 Japan

content-based image retrieval (CBIR) has been an active area of research for retrieving similar images from large repositories, without the prerequisite of manual labeling. Most current CBIR algorithms can faithfully return a list of images that matches the visual perspective of their inventors, who might decide to use a certain combination of image features like edges, colors and textures of regions as well as their spatial distribution during processing. In practice, however, the retrieved images rarely correspond exactly to the results expected by the users, a problem that has come to be known as the semantic gap. In this paper, we propose a novel and extensible multidimensional approach called matrix of visual perspectives as a solution for addressing this semantic gap. Our approach exploits the dynamic cross-interaction (in other words, mix-and-match) of image features and similarity metrics to produce results that attempt to mimic the mental visual picture of the user. Experimental results on retrieving similar Japanese cultural heritage symbols called kamons by a prototype system confirm that the interaction of visual perspectives in the user can be effectively captured and reflected. The benefits of this approach are broader. They can be equally applicable to the development of CBIR systems for other types of images, whether cultural or noncultural, by adapting to different sets of application specific image features.

关键词： content-based image retrieval cultural heritage interaction of visual perspectives

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content-based image retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

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JOURNAL OF DIGITAL IMAGING 2017年第1期30卷 63-77页

作者： Dhara, Ashis Kumar Mukhopadhyay, Sudipta Dutta, Anirvan Garg, Mandeep Khandelwal, Niranjan Indian Inst Technol Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Birla Inst Technol Mesra Elect & Commun Engn Ranchi 835215 Bihar India Post Grad Inst Med Educ & Res Dept Radio Diag & Imaging Chandigarh 160012 India

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

关键词： CT images content-based image retrieval Diagnosis of lung cancer Lung cancer Pulmonary nodules Self-learning tool of radiology CBIR based CAD system

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content-based image retrieval using fuzzy perceptual feedback

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MULTIMEDIA TOOLS AND APPLICATIONS 2007年第3期32卷 235-251页

作者： Wu, Kui Yap, Kim-Hui Nanyang Technol Univ Sch Elect & Elect Engn Media Technol Lab Singapore 639798 Singapore

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user's fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user's fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user's feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

关键词： content-based image retrieval fuzzy decision relevance feedback user information need

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content-based image retrieval using association rule mining with soft relevance feedback

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2006年第5期17卷 1108-1125页

作者： Yin, Peng-Yeng Li, Shin-Huei Natl Chi Nan Univ Dept Informat Management Puli 545 Nantou Taiwan

With the rapid development of internet technology, the transmission and access of image items have become easier and the volume of image repository is exploding. An efficient and effective query reformulation is needed for finding the relevant images from the database. Relevance feedback (RF) is an interactive process which refines the retrieval results to a particular query by utilizing the user's feedback on previously retrieved images. Most of the existing approaches deal with hard feedback (relevant and nonrelevant) and focus on individual experience only. We propose to facilitate the use of soft feedback (involving excellent, fair, don't care, and bad) to better capture user's intention. To add this feature, all of the traditional RF techniques should be modified accordingly. Further, the meta-knowledge exploited from multiple users' experiences can improve the performance of future retrieval results. We propose a soft association rule mining algorithm to infer image relevance from the collective feedback. The number of association rules is kept minimum based on confidence quantization and redundancy detection. Also, binary search and best-first search techniques are implemented to expedite the process of relevance inference from the association rules. The proposed model provides a more flexible interface for relevance feedback and the experimental results manifest that the retrieval performance of the proposed model is better than that of traditional methods. (c) 2006 Elsevier Inc. All rights reserved.

关键词： association rules confidence quantization content-based image retrieval redundancy detection soft relevance feedback

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