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content-based image retrieval through fusion of deep features extracted from segmented neutrosophic using depth map

VISUAL COMPUTER 2024年第10期40卷 6867-6881页

作者： Taheri, Fatemeh Rahbar, Kambiz Beheshtifard, Ziaeddin Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

The main challenge of content-based image retrieval systems is the difference between how images are described using algorithms and how humans understand the semantic concepts of an image. To overcome this challenge, many image retrieval methods have focused on scenarios that emphasize important regions of an image. However, losing part of the semantic features of an image is a problem that also exists in these approaches. Therefore, this article introduces a method for image retrieval using the fusion of deep features on a segmented neutrosophic set with the help of the image depth map. By transferring the original image to the neutrosophic domain, the image is decomposed into three levels: true, false, and indeterminate. True and false images have different representations of image brightness. The indeterminate image represents the boundary between the true and false images. It is also a representation of the edges in the image. Convolutional layers of deep neural networks are sensitive to changes in image brightness when extracting feature maps. For this reason, the extracted features from the true and false images are different from each other and can be considered as complementary to each other. In the second step, the image depth map is estimated using a vision transformer. Then the estimated depth map is binarized using a predefined threshold. By applying the binarized depth map to the neutrosophic domain, objects in near and far regions are classified. Effective features of each region are extracted using a pre-trained deep neural network, VGG-16. Important features from each group of images are selected using the Boruta-Shap algorithm. Finally, to reduce redundancy and unify the extracted features, feature fusion is performed in two stages, resulting in the final feature vector for each image. Experimental results confirm that extracting semantic and content features from different regions of an image using the proposed method leads to improved retrieval re

关键词： content-based image retrieval Feature fusion Neutrosophic set Depth map estimation

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

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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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content-based image retrieval Using Composite Feature Vectors with Edge Features based on Color and Pixel Similarity

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TRAITEMENT DU SIGNAL 2024年第4期41卷 1945-1952页

作者： Orman, Abdullah Ankara Yildirim Beyazit Univ Dept Comp Engn TR-06010 Ankara Turkiye

content-based image retrieval involves searching for the desired image from an image database. It is realized using feature vectors obtained from the architectural image in question. Therefore, feature extraction is a crucial step. In this study, a novel feature vector representation method is proposed. In the proposed method, a composite feature vector is obtained by using color, edge, and gradient features. The most basic feature of the proposed method is that it uses the automatic pixel similarity approach for edge detection. The automatic pixel similarity approach offers a non-linear approach similar to the human visual system. Moreover, there is no need for any parameter or user intervention in edge detection. Additionally, the computational cost is much lower than those in many iterative non-linear edge detection approaches. In the study, experiments are carried out in the Corel-1K and Corel-10K databases, which are frequently used in image retrieval. The results of the proposed method are compared to those of 13 different methods. The superior performance of the proposed method is demonstrated. The high performance and low computational cost of the proposed method show that it can be easily implemented in many real-time image retrieval systems.

关键词： content-based image retrieval edge-detection gradient human visual system ( HVS ) pixel similarity

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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 using a fusion of global and local features

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ETRI JOURNAL 2023年第3期45卷 505-518页

作者： Bu, Hee Hyung Kim, Nam Chul Kim, Sung Ho Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Kyungpook Natl Univ Sch Elect Engn Daegu South Korea

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram;the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics;and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

关键词： CLBP magnitude color autocorrelogram content-based image retrieval Gabor local correlation SVD

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content-based image retrieval using integrated features and multi-subspace randomization and collaboration

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第5期13卷 2540-2550页

作者： Kenchappa, Yashaswini Doddamane Kwadiki, Karibasappa Don Bosco Inst Technol Dept Informat Sci & Engn Kumbalagodu India Graph Era Deemed Be Univ Dept Elect & Commun Engn Dehra Dun Uttarakhand India

In the content-based image retrieval process, the semantic gap is a common challenge and the existing Bag-of-words approach is used to reduce the semantic gap. However, the computation complexity was the common problem that occurred during segmentation and the existing model underwent difficulty during the process of image retrieval. The retrieval performance influenced in exploring various sub-spaces for high dimensional data. The proposed approach performed integration of features such as local optimal oriented pattern, grey level co-occurrence matrix, and Alex net from convolution neural network with the multi-subspace randomization and Collaboration for the retrieval of semantic image. Firstly, the image segments the foreground objects from the background regions using Super Pixel-based Salience Segmentation. From the segmented region, the features present in the objects are extracted. The obtained segmented regions are used for integrating the features and will balance the data dimensions of each of the image pixels. The balanced subspace randomization schemee produces multiple partitions of features that are similar-sized random sub-spaces based on the Manhattan distance. The existing models utilized an imageNet image repository Interval Type-2 Beta fuzzy near sets Corel datasets that obtained average precision of 78.34%. Similarly, fuzzy C-Means clustering and soft label support vector machine utilized both corel and VOC that obtained average precision of 91.45% and 90.72% better when compared to the proposed method that obtained Average precision of 94.36% and 96.03% for Corel and VOC datasets.

关键词： content-based image retrieval Grey level co-occurrence matrix Local optimal oriented pattern Multi-subspace randomization and collaboration Semantic gap

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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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Archimedes Optimization Algorithm With Deep Learning Assisted content-based image retrieval in Healthcare Sector

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IEEE ACCESS 2024年 12卷 29768-29777页

作者： Issaoui, Imene Alohali, Manal Abdullah Mtouaa, Wafa Alotaibi, Faiz Abdullah Mahmud, Ahmed Assiri, Mohammed Qassim Univ Appl Coll Unit Sci Res Buraydah 52571 Saudi Arabia Univ Sfax MIRACL Lab FSEG Sfax 3029 Tunisia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Dept Math Muhayil 62529 Saudi Arabia King Saud Univ Coll Humanities & Social Sci Dept Informat Sci POB 28095 Riyadh 11437 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Al Kharj 16273 Saudi Arabia

content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an Archimedes Optimization Algorithm with Deep Learning Assisted content-based image retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models.

关键词： Feature extraction Medical services image retrieval Residual neural networks Tuning Medical diagnostic imaging Solid modeling content management Deep learning Optimization methods content-based image retrieval healthcare similarity measurement deep learning Archimedes optimization algorithm

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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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content-based image retrieval for Remote Sensing images using Hybrid Firefly and Grey Wolf Optimization Algorithm

Content-Based Image Retrieval for Remote Sensing Images usin...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Nijaguna, G.S. S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350313413

In recent years, content-based image retrieval (CBIR) has rised as an advanced approach for identifying and retrieving images from a database based on their visual content. CBIR analyzes visual material primarily using low-level properties such as color, texture, and shape. These qualities, however, may fall short in terms of capturing high-level semantics and contextual information, limiting the system's ability to fully understand the whole significance of images. To address this limitation, the hybrid Firefly and Grey Wolf Optimization (FGWO) algorithm has been proposed, which combines the Grey Wolf Optimization (GWO) with the Firefly Algorithm (FA). The FA is a nature-inspired swarm intelligence-based optimization method inspired by firefly flashing light behavior. This integration improves the image retrieval capabilities of the system. Furthermore, an Adjusted Intensity based Variant of Adaptive Histogram Equalization(AIBVAHE) filter is utilized to efficiently reduce noise in satellite images. The AIBVAHE filter improves visual contrast by redistributing intensity levels, resulting in a more uniform histogram. Color Moments and Local Binary Pattern (LBP) are utilized as feature extractors in the hybrid feature extraction approach, efficiently extracting the most relevant features suited for content-based Satellite image retrieval (CBSIR) systems. When compared to existing methods, the experimental findings show that this methodology is successful, with an impressive accuracy of 98.91%, precision of 98.41%, and recall of 98.50%. © 2023 IEEE.

关键词： Adaptive Histogram Equalization Binary Robust Independents Elementary Feature content-based image retrieval Firefly Optimization Grey Wolf Optimization

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