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Journal articles on the topic 'Image indexing'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Neugebauer, Tomasz. "Image indexing." Indexer: The International Journal of Indexing 28, no. 3 (2010): 98–103. http://dx.doi.org/10.3828/indexer.2010.28.

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Bhamre, Snehal S., and N. M. Shahane. "Image Indexing and Retrieval." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 07 (2014): 1020–23. https://doi.org/10.5281/zenodo.14742990.

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Scalable content based image search based on hash codes is hot topic nowadays. The existing hashing methods have a drawback of providing a fixed set of semantic preserving hash functions to the labelled data for the images. However, it may ignore the user’s search intention conveyed through the query image. Again these hashing methods embed high -dimensional image features into hamming space performing real time search based on hamming distance. This paper introduces a n approach that generates the most appropriate binary codes for different queries. This is done by firstly offline gener

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Rong, Li. "Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things." Mobile Information Systems 2022 (September 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/9447775.

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More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, a

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Lu, Kun, Sheng Ping Xia, and Song Hua He. "An Efficient Image Indexing Method Based on Class Specific Hyper Graph." Applied Mechanics and Materials 513-517 (February 2014): 2914–18. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2914.

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An efficient image indexing method based on class specific hyper graph is proposed. The presented indexing method works efficiently and the relevance of the original image data is enhanced. Because of that an ordered image database benefits the efficient searching. The relevance of images depends on the similarity between different images. According to clustering theory, we can take any sample image in the database as one clustering center, and then the siblings of the center and their siblings are consistently searched, which is known as similarity spread. After that, the disordered image dat

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Dam Christensen, Hans. "Rethinking image indexing?" Journal of the Association for Information Science and Technology 68, no. 7 (2017): 1782–85. http://dx.doi.org/10.1002/asi.23812.

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Safaei, Ali Asghar, and Saeede Habibi-Asl. "Multidimensional indexing technique for medical images retrieval." Intelligent Data Analysis 25, no. 6 (2021): 1629–66. http://dx.doi.org/10.3233/ida-205495.

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Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a

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Fethfulwar, Sujal. "Semantic Based Image Indexing." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1811–18. https://doi.org/10.22214/ijraset.2025.72528.

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This paper introduces a semantic-based image indexing system utilizing a custom Convolutional Neural Network (CNN) for feature extraction and semantic embedding techniques for understanding image content. Traditional image indexing methods rely heavily on low-level visual features, often resulting in inaccurate or irrelevant results. By leveraging deep learning, our proposed system bridges this gap, allowing high-level semantic features to guide indexing and retrieval. Tested on the CIFAR-10 dataset, our approach demonstrates a significant improvement in precision, recall, and overall retrieva

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Rafferty, Pauline, and Fawaz Albinfalah. "A tale of two images: the quest to create a story-based image indexing system." Journal of Documentation 70, no. 4 (2014): 605–21. http://dx.doi.org/10.1108/jd-10-2012-0130.

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Purpose – The purpose of this conceptual paper is to consider the possibility of designing a story-based image indexing system based on users’ descriptions of images. It reports a pilot study which uses users’ descriptions of two images. Design/methodology/approach – Eight interviews were undertaken to investigate storytelling in user interpretations of the images. Following this, storytelling was explored as an indexing input method. In all, 26 research subjects were asked to create stories about the images, which were then considered in relation to conventional story elements and in relation

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You, Jane, Qin Li, and Jinghua Wang. "On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 4 (2010): 18–36. http://dx.doi.org/10.4018/jcini.2010100102.

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This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity

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Zaharescu, Eugen. "Color Image Indexing Using Mathematical Morphology." Analele Universitatii "Ovidius" Constanta - Seria Matematica 22, no. 1 (2014): 281–88. http://dx.doi.org/10.2478/auom-2014-0024.

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AbstractA mathematical morphology based approach for color image indexing is explored in this paper. Morphological signatures are powerful descriptions of the image content in the framework of mathematical morphology. A morphological signature (either a pattern spectrum or a differential morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. For image indexing it is considered a morphological feature extraction algorithm which includes more complex morphological operators: i.e. color

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Jasmine Selvakumari Jeya, I., and J. Suganthi. "RONI Based Secured and Authenticated Indexing of Lung CT Images." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/830453.

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Medical images need to be transmitted with the patient’s information without altering the image data. The present paper discusses secured indexing of lung CT image (SILI) which is a secured way of indexing the lung CT images with the patient information. Authentication is provided using the sender’s logo information and the secret key is used for embedding the watermark into the host image. Watermark is embedded into the region of Noninterest (RONI) of the lung CT image. RONI is identified by segmenting the lung tissue from the CT scan image. The experimental results show that the proposed app

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Yiu, Sheung. "Hyperimage Index." A Peer-Reviewed Journal About 11, no. 1 (2022): 66–81. http://dx.doi.org/10.7146/aprja.v11i1.134309.

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Image has gone hyper, can research catch up? This essay proposes collective indexing as an alternative to academic publishing for rendering research on fast-changing and larger-than-human subjects such as algorithmic images. Following the introduction of notions of network and scale in my research, the essay articulates the value of collective indexing while mapping out contemporary examples. Collective indexing produces new ways of knowledge making and community building, as well as new forms of research aesthetics apt for addressing the distributed nature of algorithmic image systems.

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Bansode, Monika. "A survey on Content Based Image Retrieval in Cloud Environment with Privacy Preservation & Copy Deterrence." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2745–49. http://dx.doi.org/10.22214/ijraset.2021.37852.

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Abstract: Importance of images in day to day life increased tremendously. Therefore Content Based Image Retrieval studied extensively. Cloud computing offers on demand services to cloud user therefore many organizations prefer to use cloud for data storage. To protect images with sensitive or private information needs to be encrypted before being outsourced to cloud. However, this causes difficulties in image retrieval and management. The purpose of this study is to provide privacy preservation and copy deterrence Content Based Image Retrieval method using Lucene Indexing. Keywords: CBIR, Luce

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Du, M., J. Wang, C. Jing, J. Jiang, and Q. Chen. "HIERARCHICAL DATA MODEL FOR STORAGE AND INDEXING OF MASSIVE STREET VIEW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1295–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1295-2019.

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<p><strong>Abstract.</strong> Maintaining an up-to-date inventory of urban infrastructure such as fire hydrant is critical to urban management. Street view database such as Google Street View and Baidu Street View contain street-level images, their potential for urban management has not been fully explored. For the massive image, data model for storage and indexing is an important research issue. Considering multiple cameras and GPS device in the image capturing platform, a hierarchical data model named 3D-Grid is proposed. Massive street view images were stored according to

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Pentland, Alex. "Content-based indexing of images and video." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1358 (1997): 1283–90. http://dx.doi.org/10.1098/rstb.1997.0111.

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By representing image content using probabilistic models of an object's appearance we can obtain semantics–preserving compression of the image data. Such compact representations of an image's salient features allow rapid computer searches of even large image databases. Examples are shown for databases of face images, a video of American sign language (ASL), and a video of facial expressions.

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Avinash, N. Bhute, and Meshram B.B. "Text Based Approach For Indexing And Retrieval Of Image And Video: A Review." Advances in Vision Computing: An International Journal (AVC) 1, no. 1 (2014): 27–38. https://doi.org/10.5281/zenodo.3357696.

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Text data present in multimedia contain useful information for automatic annotation, indexing. Extracted information used for recognition of the overlay or scene text from a given video or image. The Extracted text can be used for retrieving the videos and images. In this paper, firstly, we are discussed the different techniques for text extraction from images and videos. Secondly, we are reviewed the techniques for indexing and retrieval of image and videos by using extracted text.

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Avinash, N. Bhute, and Meshram B.B. "Text Based Approach For Indexing And Retrieval Of Image And Video: A Review." Advances in Vision Computing: An International Journal (AVC) 1, no. 1 (2014): 27–38. https://doi.org/10.5281/zenodo.3554868.

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Text data present in multimedia contain useful information for automatic annotation, indexing. Extracted information used for recognition of the overlay or scene text from a given video or image. The Extracted text can be used for retrieving the videos and images. In this paper, firstly, we are discussed the different techniques for text extraction from images and videos. Secondly, we are reviewed the techniques for indexing and retrieval of image and videos by using extracted text.

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Beyerlein, Kenneth R., Thomas A. White, Oleksandr Yefanov, et al. "FELIX: an algorithm for indexing multiple crystallites in X-ray free-electron laser snapshot diffraction images." Journal of Applied Crystallography 50, no. 4 (2017): 1075–83. http://dx.doi.org/10.1107/s1600576717007506.

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A novel algorithm for indexing multiple crystals in snapshot X-ray diffraction images, especially suited for serial crystallography data, is presented. The algorithm, FELIX, utilizes a generalized parametrization of the Rodrigues–Frank space, in which all crystal systems can be represented without singularities. The new algorithm is shown to be capable of indexing more than ten crystals per image in simulations of cubic, tetragonal and monoclinic crystal diffraction patterns. It is also used to index an experimental serial crystallography dataset from lysozyme microcrystals. The increased numb

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Ginn, Helen Mary, Philip Roedig, Anling Kuo, et al. "TakeTwo: an indexing algorithm suited to still images with known crystal parameters." Acta Crystallographica Section D Structural Biology 72, no. 8 (2016): 956–65. http://dx.doi.org/10.1107/s2059798316010706.

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The indexing methods currently used for serial femtosecond crystallography were originally developed for experiments in which crystals are rotated in the X-ray beam, providing significant three-dimensional information. On the other hand, shots from both X-ray free-electron lasers and serial synchrotron crystallography experiments are still images, in which the few three-dimensional data available arise only from the curvature of the Ewald sphere. Traditional synchrotron crystallography methods are thus less well suited to still image data processing. Here, a new indexing method is presented wi

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20

Beebe, Caroline. "Image Indexing for Multiple Needs." Art Documentation: Journal of the Art Libraries Society of North America 19, no. 2 (2000): 16–21. http://dx.doi.org/10.1086/adx.19.2.27949079.

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21

Guoping Qiu. "Color image indexing using BTC." IEEE Transactions on Image Processing 12, no. 1 (2003): 93–101. http://dx.doi.org/10.1109/tip.2002.807356.

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22

Mirmehdi, Majid, and Radhakrishnan Perissamy. "Perceptual Image Indexing and Retrieval." Journal of Visual Communication and Image Representation 13, no. 4 (2002): 460–75. http://dx.doi.org/10.1006/jvci.2002.0506.

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Ciocca, Gianluigi, Claudio Cusano, Simone Santini, and Raimondo Schettini. "Image Indexing Using Prosemantic Features." Archiving Conference 11, no. 1 (2014): 88–93. http://dx.doi.org/10.2352/issn.2168-3204.2014.11.1.art00019.

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24

Graham, Margaret E. "The cataloguing and indexing of images: time for a new paradigm?" Art Libraries Journal 26, no. 1 (2001): 22–27. http://dx.doi.org/10.1017/s0307472200012001.

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Funding opportunities and digitisation initiatives offer libraries, galleries and museums the potential to exploit their image collections – photographs, slides, drawings, pictures and works of art – in new and exciting ways. Many different organisations are involved in developing standards for the formal description of images (e.g. artist, title, photographer) and some effort is being made to develop compatible standards for the digital environment. Indexing of images can be a difficult task because images are rich in information and may be used by widely different groups of users, who may no

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Mathan Kumar, B., and R. PushpaLakshmi. "An Approach for Image Search and Retrieval by Cluster-Based Indexing of Binary MKSIFT Codes." Computer Journal 63, no. 6 (2020): 857–79. http://dx.doi.org/10.1093/comjnl/bxz145.

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Abstract Image search is an information retrieval approach that gained remarkable attention in the areas like multimedia and computer vision. The first work presented a cross-indexing-based approach for image retrieval using multiple kernel scale-invariant feature transform (MKSIFT), where the key point descriptor was calculated using two kernel functions. Previous work had complexity issues while dealing with the large databases, and hence, to avoid this, cluster-based indexing of binary MKSIFT codes is presented. The proposed cluster-based indexing scheme uses the MKSIFT feature extraction a

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Guru, D. S., K. B. Nagasundara, S. Manjunath, and R. Dinesh. "An Approach for Hand Vein Representation and Indexing." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 1–15. http://dx.doi.org/10.4018/jdcf.2011040101.

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This paper proposes a model for representing and indexing of hand vein images. The proposed representation model identifies the junction points and perceives the spatial relationships existing among all junction points in hand vein images by the use of triangular spatial relationship (TSR). The model preserves the TSR among the junction points in a symbolic hand vein image by the use of quadruples and for each quadruple, a unique TSR key is generated. A novel methodology to label the junction points based on graph properties of junction points is also proposed. A Symbolic Hand Vein Image Datab

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Bokare, Dr Madhav M. "Markov Chains for User Queries Mining: Image Retrieval Application using Internet." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2455–71. http://dx.doi.org/10.22214/ijraset.2024.63382.

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Abstract: We suggest a brand-new technique for automatically annotating, indexing, and retrieving photos based on annotations. We introduce the novel technique, which we term Markovian Semantic Indexing (MSI), within the framework of an image retrieval system in online mode. In the event that such a system exists, the queries entered by the users are utilised to build an Aggregate Markov Chain (AMC), which defines the significance between the terms the system sees. The photographs are automatically annotated based on the queries entered by the users. Next, depending on the annotation of each i

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Roy, Dibyendu, Niladri Halder, and Poushali Roy. "HRTEM Based SADP Image Indexing Using Image Processing." IOSR Journal of Electrical and Electronics Engineering 11, no. 04 (2016): 23–28. http://dx.doi.org/10.9790/1676-1104032328.

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Xu, Wang Ming, Kang Ling Fang, and Hai Ru Zhang. "An Efficient Hierarchical Clustering Algorithm and its Application for Image Retrieval." Applied Mechanics and Materials 44-47 (December 2010): 3757–61. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3757.

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Clustering is an efficient and fundamental unsupervised learning algorithm for many vision-based applications. This paper aim at the problems of fast indexing high-dimensional local invariant features of images (e.g. SIFT features) and quick similarity searching of images in a scalable image database by using a hierarchical clustering algorithm. We adopt the hierarchical K-means (HKM) clustering method to build a visual vocabulary tree efficiently on given training data and represent image as a “bag of visual words” which are the leaf nodes of the visual vocabulary tree. For the application of

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Nubila, B. Di, I. Gagliardi, D. Macchi, L. Milanesi, M. Padula, and R. Pagani. "Concept-based indexing and retrieval of multimedia documents." Journal of Information Science 20, no. 3 (1994): 185–96. http://dx.doi.org/10.1177/016555159402000304.

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In this work, we face the problem of multimedia document indexing with reference to a specific application field, the radiological ward where automatic information management by content is an urgent need. Here, a multimedia document is composed of text and images. The keystone of the approach is the image indexing which is performed in an indirect way: the description of the image (made by an expert, in our case a physician) is further synthesised and formalised to be used by the computer. In this Paper, we propose a concept-based indexing of the description of the images which is based on Far

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Patwal, Pratap Singh, Jitendra Kumar Tyagi, and Garima Yadav. "Image Indexing System Using Texture Mining." International Journal of Computer Sciences and Engineering 6, no. 5 (2018): 357–60. http://dx.doi.org/10.26438/ijcse/v6i5.357360.

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Cawkell, Tony. "Image indexing and retrieval by content." Information Services & Use 20, no. 1 (2000): 49–58. http://dx.doi.org/10.3233/isu-2000-20106.

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Mandal, M. K., T. Aboulnasr, and S. Panchanathan. "Image indexing using moments and wavelets." IEEE Transactions on Consumer Electronics 42, no. 3 (1996): 557–65. http://dx.doi.org/10.1109/30.536156.

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Berens, J., G. D. Finlayson, and G. Qiu. "Image indexing using compressed colour histograms." IEE Proceedings - Vision, Image, and Signal Processing 147, no. 4 (2000): 349. http://dx.doi.org/10.1049/ip-vis:20000630.

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Singh Patwal, Pratap, and A. K. Srivastava. "Content based Image Indexing & Retrieval." International Journal of Computer & Organization Trends 16, no. 1 (2015): 25–29. http://dx.doi.org/10.14445/22492593/ijcot-v16p305.

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Climer, Sharlee, and Sanjiv K. Bhatia. "Image database indexing using JPEG coefficients." Pattern Recognition 35, no. 11 (2002): 2479–88. http://dx.doi.org/10.1016/s0031-3203(01)00182-0.

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Tsapatsoulis, N., Y. Avrithis, and S. Kollias. "Facial Image Indexing in Multimedia Databases." Pattern Analysis & Applications 4, no. 2-3 (2001): 93–107. http://dx.doi.org/10.1007/pl00014577.

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Gevers, Theo, Graham Finlayson, and Raimondo Schettini. "Color for Image Indexing and Retrieval." Computer Vision and Image Understanding 94, no. 1-3 (2004): 1–2. http://dx.doi.org/10.1016/j.cviu.2004.03.001.

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Vailaya, A., M. A. T. Figueiredo, A. K. Jain, and Hong-Jiang Zhang. "Image classification for content-based indexing." IEEE Transactions on Image Processing 10, no. 1 (2001): 117–30. http://dx.doi.org/10.1109/83.892448.

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Douze, Matthijs, and Hervé Jégou. "Image indexing and retrieval with Yael." ACM SIGMultimedia Records 7, no. 2 (2015): 9–14. http://dx.doi.org/10.1145/2835398.2835405.

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Babu, G. Phanendra, Babu M. Mehtre, and Mohan S. Kankanhalli. "Color indexing for efficient image retrieval." Multimedia Tools and Applications 1, no. 4 (1995): 327–48. http://dx.doi.org/10.1007/bf01215882.

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Dorsch, Isabelle, Franziska Zimmer, and Wolfgang G. Stock. "Image indexing through hashtags in instagram." Proceedings of the Association for Information Science and Technology 54, no. 1 (2017): 658–59. http://dx.doi.org/10.1002/pra2.2017.14505401105.

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Gelernter, Judith. "Image indexing in article component databases." Journal of the American Society for Information Science and Technology 60, no. 10 (2009): 1965–76. http://dx.doi.org/10.1002/asi.21107.

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Lin, Shinfeng D., Kuo-Hae Chen, and Xin-Lun Yang. "Image indexing by Color Plane Moment." International Journal of Imaging Systems and Technology 12, no. 4 (2002): 139–48. http://dx.doi.org/10.1002/ima.10022.

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Sharvit, Daniel, Jacky Chan, Hüseyin Tek, and Benjamin B. Kimia. "Symmetry-Based Indexing of Image Databases." Journal of Visual Communication and Image Representation 9, no. 4 (1998): 366–80. http://dx.doi.org/10.1006/jvci.1998.0396.

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Wu, Yu Tzu, Eric Fujiwara, and Carlos Kenichi Suzuki. "Image-Based Radical Identification in Chinese Characters." Applied Sciences 13, no. 4 (2023): 2163. http://dx.doi.org/10.3390/app13042163.

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The Chinese writing system, known as hanzi or Han character, is fundamentally pictographic, composed of clusters of strokes. Nowadays, there are over 85,000 individual characters, making it difficult even for a native speaker to recognize the precise meaning of everything one reads. However, specific clusters of strokes known as indexing radicals provide the semantic information of the whole character or even of an entire family of characters, are golden features in entry indexing in dictionaries and are essential in learning the Chinese language as a first or second idiom. Therefore, this wor

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Katta, Sugamya. "Image Retrieval using Hybrid Order Dither Block Truncation Code." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2049–54. http://dx.doi.org/10.22214/ijraset.2021.35449.

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A new approach is proposed to index images in database using features generated from the HODBTC compressed data stream. This indexing technique can be extended for CBIR. HODBTC compresses an image into a set of color quantizers and a bitmap image. The proposed image retrieval system generates two image features namely CCF and BPF from the minimum quantizer, maximum quantizer and bitmap image respectively by involving the visual codebook.

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Uervirojnangkoorn, Monarin, Artem Y. Lyubimov, Qiangjun Zhou, William I. Weis, and Axel T. Brunger. "Resolving indexing ambiguities in X-ray free-electron laser diffraction patterns." Acta Crystallographica Section D Structural Biology 75, no. 2 (2019): 234–41. http://dx.doi.org/10.1107/s2059798318013177.

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Processing X-ray free-electron laser (XFEL) diffraction images poses challenges, as an XFEL pulse is powerful enough to destroy or damage the diffracting volume and thereby yields only one diffraction image per volume. Moreover, the crystal is stationary during the femtosecond pulse, so reflections are generally only partially recorded. Therefore, each XFEL diffraction image must be scaled individually and, ideally, corrected for partiality prior to merging. An additional complication may arise owing to indexing ambiguities when the symmetry of the Bravais lattice is higher than that of the sp

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Gao, Rong Hua, and Hua Rui Wu. "Image Characteristics Indexing Based on X-Tree." Applied Mechanics and Materials 513-517 (February 2014): 3761–64. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3761.

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Image data set are usually very large, which might consist of millions of image objects, it is essential to use an efficient and effective indexing technique to facilitate speedy searching. The features can be expressed in terms of high-dimensional vector data which can be compared with a given query for similarity between them. It is more important that the image database should be preprocessed and establish indexing to improve retrieval efficiency. In this paper, the method of improved X-tree is proposed, design and implementation of a high dimensional index application to facilitate the spe

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Sun, Jian Fei, Zhi Yi Qu, and Kun Yu Wang. "Fast Image Retrieval Based Weighted Color AutoCorrelogram and LSH Indexing." Applied Mechanics and Materials 667 (October 2014): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.667.208.

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With the Rapid Development of Internet, Image Retrieval is more and more Important,For Image Retrieval, it Requires Not only a Real-Time Retrieval Speed but also the Accuracy of the Results.The Method Described in this Article: Firstly,We should Quantify the HSV Color Space of the Image Non-Uniformly,and then Extract the Weighted Color Autocorrelogram ,and Finally Build an Indexing Library Based Locality-Sensitive Hashing.The Method Successfully Solves the Problem of Dimension Disaster.With Different Scales Image Datasets，the Experimental Results Show that the Method can Retrieve Similar Image

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