Relevant bibliographies by topics / Image indexing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image indexing'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Image indexing"

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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 generating bitwise we ights of the hash codes for a set of predefined semantic classes. At query time, query adaptive weights are computed online by finding out the proximity between a query and the semantic concept classes. Then these images can be ranked by weighted Hamming distance at a finer-grained hash code level rather than the original Hamming distance level 
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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, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.
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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 database is sorted out and the searching result is not tedious any more. The proposed method has been tested by an open arts press image database, which shows that our method can obviously improve the indexing speed. Moreover, the indexing results make the whole image searching system capable of association.
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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 multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.
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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 to Hidderley and Rafferty's (2005) image modality model. Findings – The results of the semi-structured interviews revealed that the majority of interpretations incorporated story elements related to setting, character, plot, literary devices, and themes. The 52 image stories included story elements identified in the first part of the project, and suggested that the image modality model is robust enough to deal with the “writerly” images used in this study. In addition, using storytelling as an input method encourages the use of verbs and connotative level responses. Originality/value – User indexing is generally based on paradigmatic approaches to concept analysis and interpretation in the form of tagging; the novelty of the current study is its exploration of syntagmatic approaches to user indexing in the form of storytelling. It is a pilot, proof of concept study, but it is hoped that it might stimulate further interest in syntagmatic approaches to user indexing.
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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 measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.
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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 gradient, homotopic skeleton, Hit-or-Miss transform. In the end, illustrative application examples of the presented approach on real acquired images are also provided.
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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 approach is robust against unauthorized access, noise, blurring, and intensity based attacks.
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Dissertations / Theses on the topic "Image indexing"

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Taner, Serdar. "Image Classification For Content Based Indexing." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/2/1093269/index.pdf.

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As the size of image databases increases in time, the need for content based image indexing and retrieval become important. Image classification is a key to content based image indexing. In this thesis supervised learning with feed forward back propagation artificial neural networks is used for image classification. Low level features derived from the images are used to classify the images to interpret the high level features that yield semantics. Features are derived using detail histogram correlations obtained by Wavelet Transform, directional edge information obtained by Fourier Transform and color histogram correlations. An image database consisting of 357 color images of various sizes is used for training and testing the structure. The database is indexed into seven classes that represent scenery contents which are not mutually exclusive. The ground truth data is formed in a supervised fashion to be used in training the neural network and testing the performance. The performance of the structure is tested using leave one out method and comparing the simulation outputs with the ground truth data. Success, mean square error and the class recall rates are used as the performance measures. The performances of the derived features are compared with the color and texture descriptors of MPEG-7 using the structure designed. The results show that the performance of the method is comparable and better. This method of classification for content based image indexing is a reliable and valid method for content based image indexing and retrieval, especially in scenery image indexing.
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Berens, Jeff. "Image indexing using compressed colour histograms." Thesis, University of East Anglia, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251710.

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Idris, Fayez M. "Image and video indexing using vector quantization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0018/NQ48104.pdf.

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Bosilj, Petra. "Image indexing and retrieval using component trees." Thesis, Lorient, 2016. http://www.theses.fr/2016LORIS396/document.

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Cette thèse explore l’utilisation de représentations hiérarchiques des images issues de la morphologie mathématique, les arbres des coupes, pour la recherche et la classification d’images. Différents types de structures arborescentes sont analysés et une nouvelle classification en deux superclasses est proposée, ainsi qu’une contribution à l’indexation et à la représentation de ces structures par des dendogrammes. Deux contributions à la recherche d’images sont proposées, l’une sur la détection de régions d’intérêt et l’autre sur la description de ces régions. Les régions MSER peuvent être détectées par un algorithme s’appuyant sur une représentation des images par arbres min et max. L’utilisation d’autres structures arborescentes sous-jacentes permet de détecter des régions présentant des propriétés de stabilité différentes. Un nouveau détecteur, basé sur les arbres des formes, est proposé et évalué en recherche d’images. Pour la description des régions, le concept de spectres de formes 2D permettant de décrire globalement une image est étendu afin de proposer un descripteur local, au pouvoir discriminant plus puissant. Ce nouveau descripteur présente de bonnes propriétés à la fois de compacité et d’invariance à la rotation et à la translation. Une attention particulière a été portée à la préservation de l’invariance à l’échelle. Le descripteur est évalué à la fois en classification d’images et en recherche d’images satellitaires. Enfin, une technique de simplification des arbres de coupes est présentée, qui permet à l’utilisateur de réévaluer les mesures du niveau d’agrégation des régions imposé par les arbres des coupes<br>This thesis explores component trees, hierarchical structures from Mathematical Morphology, and their application to image retrieval and related tasks. The distinct component trees are analyzed and a novel classification into two superclasses is proposed, as well as a contribution to indexing and representation of the hierarchies using dendrograms. The first contribution to the field of image retrieval is in developing a novel feature detector, built upon the well-established MSER detection. The tree-based implementation of the MSER detector allows for changing the underlying tree in order to produce features of different stability properties. This resulted in the Tree of Shapes based Maximally Stable Region detector, leading to improvements over MSER in retrieval performance. Focusing on feature description, we extend the concept of 2D pattern spectra and adapt their global variant to more powerful, local schemes. Computed on the components of Min/Max-tree, they are histograms holding the information on distribution of image region attributes. The rotation and translation invariance is preserved from the global descriptor, while special attention is given to achieving scale invariance. We report comparable results to SIFT in image classification, as well as outperforming Morphology-based descriptors in satellite image retrieval, with a descriptor shorter than SIFT. Finally, a preprocessing or simplification technique for component trees is also presented, allowing the user to reevaluate the measures of region level of aggregation imposed on a component tree. The thesis is concluded by outlining the future perspectives based on the content of the thesis
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Schultz, Leah. "Image manipulation and user-supplied index terms." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc9828/.

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This study investigates the relationships between the use of a zoom tool, the terms they supply to describe the image, and the type of image being viewed. Participants were assigned to two groups, one with access to the tool and one without, and were asked to supply terms to describe forty images, divided into four categories: landscape, portrait, news, and cityscape. The terms provided by participants were categorized according to models proposed in earlier image studies. Findings of the study suggest that there was not a significant difference in the number of terms supplied in relation to access to the tool, but a large variety in use of the tool was demonstrated by the participants. The study shows that there are differences in the level of meaning of the terms supplied in some of the models. The type of image being viewed was related to the number of zooms and relationships between the type of image and the number of terms supplied as well as their level of meaning in the various models from previous studies exist. The results of this study provide further insight into how people think about images and how the manipulation of those images may affect the terms they assign to describe images. The inclusion of these tools in search and retrieval scenarios may affect the outcome of the process and the more collection managers know about how people interact with images will improve their ability to provide access to the growing amount of pictorial information.
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Teng, Shyh Wei 1973. "Image indexing and retrieval based on vector quantization." Monash University, Gippsland School of Computing and Information Technology, 2003. http://arrow.monash.edu.au/hdl/1959.1/5764.

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Armstrong, Andrew. "Image indexing and retrieval in the compressed domain." Thesis, University of South Wales, 2003. https://pure.southwales.ac.uk/en/studentthesis/image-indexing-and-retrieval-in-the-compressed-domain(3601fe0d-cb77-4031-855b-a9e9125ec211).html.

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This thesis is focused on low computational cost algorithms to facilitate the automatic indexing and retrieval of digital images. Several techniques are proposed, which can be classified into three distinct stages of application; In the first stage, a novel approach is proposed to provide a foundation for using genetic algorithms in imaging applications. The approach allows itself to be applied readily to any block based quantisation problem, including that of JPEG DCT domain information. The second stage tackles the main problem investigated, that of how to automatically index large numbers of images reliably and provide a mechanism to retrieve those images. Several methods are proposed, which apply various indexing techniques to JPEG images by the extraction of keys directly from the DCT domain. Thus avoiding the computationally costly de-compression stage required when applying pixel based techniques and serves the creation of index keys from partially-decoded data through manipulation of DCT coefficients - to increase speed and improve processing costs. The use of neural and genetic techniques to extract and organise DCT coefficients renders de-compression unnecessary. The third stage focuses on image security. Proposed techniques are developed and analysed to allow for the inclusion of watermarking and other data. Using these techniques, a substantial proportion of non-image data can be stored within the image invisibly at little or no extra cost. These allow additional security to be imposed, coupling these with the indexing methods; image tracking and change control can be implemented with very little overhead. All the work presented lends itself easily to a number of domains including web databases, general storage, diagnosis and secure image tracking. More importantly, extensive experimentation and analysis prove that the proposed techniques are faster than their counterparts with no loss in the quality of results generated.
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Solli, Martin. "Color Emotions in Large Scale Content Based Image Indexing." Doctoral thesis, Linköpings universitet, Medie- och Informationsteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-64591.

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Traditional content based image indexing aims at developing algorithms that can analyze and index images based on their visual content. A typical approach is to measure image attributes, like colors or textures, and save the result in image descriptors, which then can be used in recognition and retrieval applications. Two topics within content based image indexing are addressed in this thesis: Emotion based image indexing, and font recognition. The main contribution is the inclusion of high-level semantics in indexing of multi-colored images. We focus on color emotions and color harmony, and introduce novel emotion and harmony based image descriptors, including global emotion histograms, a bag-of-emotions descriptor, an image harmony descriptor, and an indexing method based on Kobayashi's Color Image Scale. The first three are based on models from color science, analyzing emotional properties of single colors or color combinations. A majority of the descriptors are evaluated in psychophysical experiments. The results indicate that observers perceive color emotions and color harmony for multi-colored images in similar ways, and that observer judgments correlate with values obtained from the presented descriptors. The usefulness of the descriptors is illustrated in large scale image classification experiments involving emotion related image categories, where the presented descriptors are compared with global and local standard descriptors within this field of research. We also investigate if these descriptors can predict the popularity of images. Three image databases are used in the experiments, one obtained from an image provider, and two from a major image search service. The two from the search service were harvested from the Internet, containing image thumbnails together with keywords and user statistics. One of them is a traditional object database, whereas the other is a unique database focused on emotional image categories. A large part of the emotion database has been released to the research community. The second contribution is visual font recognition. We implemented a font search engine, capable of handling very large font databases. The input to the search engine is an image of a text line, and the output is the name of the font used when rendering the text. After pre-processing and segmentation of the input image, eigenimages are used, where features are calculated for individual characters. The performance of the search engine is illustrated with a database containing more than 2700 fonts. A system for visualizing the entire font database is also presented. Both the font search engine, and the descriptors that are related to emotions and harmony are implemented in publicly available search engines. The implementations are presented together with user statistics.
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Manerba, Francesca. "Efficient object identification in image sequences for content indexing." Bordeaux 1, 2005. http://www.theses.fr/2005BOR13065.

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"Les avancées en matière d'acquisition et de stockage des données ont conduit à la disponibilité de grandes bases de données vidéo numériques pour l'utilisateur grand-public. Néanmoins l'interaction avec les données multimédia et vidéo en particulier nécessite des outils de description, organisation et gestion de ces données vidéo. La majorité des documents multimédia sont produits sous forme compréssée. De plus comme les utilisateurs demandent des réponses à leur requêtes les plus rapides possible, l'extraction d'information en temps réel même si celle-ci n'est pas très précise est devenue un objectif important. Basée sur des hypothèses formulées ci-dessus, une nouvelle tendance en matière d'analyse pour l'indexation multimédia est parue, elle peut être qualifiée comme paradigme de "rough indexing" ou "indexation grossière". Nous pouvons inclure notre travail dans cette mouvance d'indexation rapide et approximative dans laquelle uniquement les données imprécises telles que les vecteurs du mouvement et les images DC sont exploitées pour produire une indexation fine des objets en avant-plan. Dans ce paradigme nous proposons de combiner à la fois les informations du mouvement et la segmentation basée-couleur pour extraire les objets pertinents des flux vidéo compressés. Il peut arriver que le mouvement d'un objet soit très similaire au mouvement de la caméra ou que l'objet soit statique, aucun objet ne sera alors détecté. Néanmoins, comme un objet ne peut raisonnablement pas apparaître et disparaitre durant une coute séquence d'images, nous proposons de filtrer les séquences d'objets le long de l'axe temporel. Pour ce faire nous proposons une modélisation de la séquence des objets par une super-quadrique. La méthode proposée donne des résultats prometteurs : les ratés de la détection ou les sur-détections peuvent être corrigés par la méthode de filtrage spatio-temporel proposée. "
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Skarpathiotis, Constantinos. "A hardware implementation of an image and video indexing system." Thesis, University of Kent, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413058.

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Books on the topic "Image indexing"

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Jean-Luc, Dugelay, Baskurt Atilla 1960-, and Daoudi Mohamed Ph D, eds. 3D object processing: Compression, indexing, and watermarking. J. Wiley & Sons, 2008.

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Alistair, Moffat, and Bell Timothy C, eds. Managing gigabytes: Compressing and indexing documents and images. Van Nostrand Reinhold, 1994.

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1945-, Fidel Raya, and American Society for Information Science., eds. Challenges in indexing electronic text and images. Published for the American Society for Information Science by Learned Information, 1994.

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Cisco, Susan Lynn. Indexing documents for imaging systems: A roadmap to success. Marketfinders, 1993.

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Jörgensen, Corinne. Image attributes: An investigation. UMI, 2000.

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Urbana-Champaign), Clinic on Library Applications of Data Processing (33rd 1996 University of Illinois at. Digital image access & retrieval. Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, 1997.

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Thompson, Nicholas. If a picture's worth a thousand words--: Problems in the indexing of the visual image and some solutions. Dept. of Library and Information Studies, Victoria University of Wellington, 1991.

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Fletcher, Glennys. Indexing digital skating images. University of Central England in Birmingham, 2000.

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Cawkell, A. E. Indexing collections of electronic images: A review. British Library, Research and Development Dept., 1993.

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Graham, Margaret E. Description and indexing of images: Report of a survey of ARLIS members, 1998/99. Institute for Image Data Research, University of Northumbria, 1999.

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Book chapters on the topic "Image indexing"

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Zhang, Dengsheng. "Image Indexing." In Texts in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_11.

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Zhang, Dengsheng. "Image Indexing." In Texts in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69251-3_11.

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Scherer, Rafał. "Image Indexing Techniques." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12195-2_3.

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Manolopoulos, Yannis, Yannis Theodoridis, and Vassilis J. Tsotras. "Image and Multimedia Indexing." In Advanced Database Indexing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8590-3_8.

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Nes, Niels, and Marcos Cordeiro d’Ornellas. "Color Image Texture Indexing." In Visual Information and Information Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48762-x_58.

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Bertino, Elisa, Beng Chin Ooi, Ron Sacks-Davis, et al. "Image Databases." In Indexing Techniques for Advanced Database Systems. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6227-6_3.

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Yilmaz, Fadi, and M. Fatih Demirci. "Indexing Tree Structures through Caterpillar Decomposition." In Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_64.

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Gong, Yihong. "Indexing Structures for Image Databases." In Intelligent Image Databases. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5479-0_3.

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Uegaki, Naoki, Masao Izumi, and Kunio Fukunaga. "Multimodal Automatic Indexing for Broadcast Soccer Video." In Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_81.

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Della Ventura, Anna, Isabella Gagliardi, and Raimondo Schettini. "Indexing Color-Texture Image Patterns." In Image Description and Retrieval. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4825-6_5.

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Conference papers on the topic "Image indexing"

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Saouabe, Abdelkrim, Said Tkatek, Hicham Oualla, and Imad Mourtaji. "Image Indexing Approaches for Enhanced Content-Based Image Retrieval: An Overview." In 2024 International Conference on Ubiquitous Networking (UNet). IEEE, 2024. https://doi.org/10.1109/unet62310.2024.10794711.

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Finlayson, Graham D., and Gui Yun Tian. "Colour Indexing Across Illumination." In Challenge of Image Retrieval. BCS Learning & Development, 1999. http://dx.doi.org/10.14236/ewic/cir1999.7.

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Finlayson, G. D. "Colour-texture indexing." In IEE Colloquium on Intelligent Image Databases. IEE, 1996. http://dx.doi.org/10.1049/ic:19960750.

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"Image indexing and retrieval." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586837.

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Finlayson, Graham D., Bernt Schiele, and James L. Crowley. "Using Colour for Image Indexing." In Challenge of Image Retrieval. BCS Learning & Development, 1998. http://dx.doi.org/10.14236/ewic/cir1998.5.

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Yang, Kuiyuan, Meng Wang, and Hong-Jiang Zhang. "Active tagging for image indexing." In 2009 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2009. http://dx.doi.org/10.1109/icme.2009.5202829.

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Yilmaz, Fadi, and M. Fatih Demirci. "Image indexing with caterpillar decomposition." In 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011. http://dx.doi.org/10.1109/siu.2011.5929586.

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Idris, Fayez M., and Sethuraman Panchanathan. "Image indexing using vector quantization." In IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology, edited by Wayne Niblack and Ramesh C. Jain. SPIE, 1995. http://dx.doi.org/10.1117/12.205307.

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Zhao, Qian, and Hui-li Guo. "Image Indexing Using Shape Feature." In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2011. http://dx.doi.org/10.1109/wicom.2011.6040542.

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Swain, Michael J. "Interactive indexing into image databases." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Carlton W. Niblack. SPIE, 1993. http://dx.doi.org/10.1117/12.143659.

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Reports on the topic "Image indexing"

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Dickinson, Sven J., and Suzanne Stevenson. Viewpoint - Invariant Indexing for Content Based Image Retreival. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada391690.

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Kwong, M. K., and B. Lin. Large-scale indexing and retrieval system for local image features. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/505383.

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Stiller, Peter. Algebraic Geometry and Computational Algebraic Geometry for Image Database Indexing, Image Recognition, And Computer Vision. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada384588.

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Ben-Arie, Jezekiel, and G. Atkin. Automatic Target Recognition and Indexing by Non-Orthogonal Image Expansion and Data-Dependent Normalization with Implementation. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada333426.

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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Abstract:
Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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Zheng, Yefeng. Handwriting Identification, Matching, and Indexing in Noisy Document Images. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada447910.

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Doermann, David. The Indexing and Retrieval of Document Images: A Survey. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada458104.

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