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Journal articles on the topic 'Content-based search and retrieval'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Varma, Ankitha, and Dr Kamalpreet Kaur. "Survey on content based image retrieval." International Journal of Engineering & Technology 7, no. 4.5 (2018): 471. http://dx.doi.org/10.14419/ijet.v7i4.5.21136.

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Now-a-days, because of the advancement in the digital technology and the use of internet, a huge amount of digital data is available in the form of medical images, remote sensing, digital museums, geographical information, etc. This has lead to the need of accurate and efficient techniques for the search and retrieval of relevant images from such voluminous datasets. Content based image retrieval (CBIR) is one such approach which is increasingly being used to search and retrieve query image from the databases. CBIR combines features of color, texture as well as shape which ease out the process

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Jasmine K. S., Rishav Raj, and Mahalakshmi Mabla Naik. "A New Content-Based Search Mechanism for Image Retrieval Search Engine." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijirr.289611.

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In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% a

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Dr., Aziz Makandar, Rashmi Somshekhar Mrs., and Nayan Jadav Miss. "Content Based Image Retrieval." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 1151–54. https://doi.org/10.31142/ijtsrd24047.

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The incremented desideratum of content based image retrieval system can be found in a number of different domains such as Data Mining, Edification, Medical Imaging, Malefaction Aversion, climate, Remote Sensing and Management of Globe Resources. Google's image search and photo album implements such as image search, Google's Picasa project applications in general gregarious networking environment, the hunt for practical, efficacious image search in the web context. Our application provides the color based image retrieval, utilizing features like dominant color. The color features are ob

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Fouad, Mohamed M. "Content-based Search for Image Retrieval." International Journal of Image, Graphics and Signal Processing 5, no. 11 (2013): 46–52. http://dx.doi.org/10.5815/ijigsp.2013.11.05.

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Roshni, S. Tadse* L. H. Patil C. U. Chauhan. "CONTENT BASED INFORMATION RETRIEVAL FOR DIGITAL LIBRARY USING DOCUMENT IMAGE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 632–38. https://doi.org/10.5281/zenodo.57052.

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In the recent year, the using of mobile devices has perceive an emerging need for improving the user experience of digital library for search, with various applications such as education, location search and product retrieval, There  simply compare the query to the databases images; those are match that images are retrieve from the database, searching and response time of delivery staying a challenging issues in mobile document search previously lots of work has been done on search engine, retrieving  the document from the database without analyzed the image.  In The proposed me

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Lin, Zhi Chao, Lei Sun, and Xiao Liu. "Research and Improvement on Content-Based Web Search Engine." Advanced Materials Research 532-533 (June 2012): 1282–86. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1282.

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There is a lot of information contained in the World Wide Web. It has become a research focus to obtain the required related resources quickly and accurately from the web through the content-based search engines. Most current tools of full text web search engine, such as Lucene which is a widely used open source retrieval library in information retrieval field, are purely keyword based. This may not sufficient for users to retrieve in the web. In this paper, we employ a method to overcome the limitations of current full text search engines in represent of Lucene. We propose a Query Expansion a

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Mai, Nicole Tham Ley, Syahmi Syahiran Bin Ahmad Ridzuan, and Zaid Bin Omar. "Content-based Image Retrieval System for an Image Gallery Search Application." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1903. http://dx.doi.org/10.11591/ijece.v8i3.pp1903-1912.

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Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient brow

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Sheetal, Deepak Patil. "Content Based Image and Video Retrieval: A Compressive Review." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 243–47. https://doi.org/10.35940/ijeat.E2783.0610521.

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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d

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Pao, Hsiao, and Hsin Fu. "Content-based Search for Effective Image Retrieval." International Conference on Electrical Engineering 7, no. 7 (2010): 1–10. http://dx.doi.org/10.21608/iceeng.2010.33291.

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Zhou, Zhengzhong, and Liqing Zhang. "Content-Based Image Retrieval Using Iterative Search." Neural Processing Letters 47, no. 3 (2017): 907–19. http://dx.doi.org/10.1007/s11063-017-9662-y.

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Chakraverti, Ashish. "Deep Learning based Smart Image Search Engine." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1577–85. http://dx.doi.org/10.22214/ijraset.2024.58602.

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Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Text

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Gupta, Rajeev, and Virender Singh. "COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE." International Journal of Students' Research in Technology & Management 8, no. 1 (2020): 01–10. http://dx.doi.org/10.18510/ijsrtm.2020.811.

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Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval tech

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Patil, Sheetal Deepak. "Content Based Image and Video Retrieval A Compressive Review." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 243–47. http://dx.doi.org/10.35940/ijeat.e2783.0610521.

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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy d

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Szűcs, Gábor, and Dávid Papp. "Content-Based Image Retrieval for Multiple Objects Search." Cybernetics and Information Technologies 17, no. 2 (2017): 106–18. http://dx.doi.org/10.1515/cait-2017-0020.

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Abstract The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image search system, which is capable of multiple object retrieval using only visual content of the images. We have used the state-of-the-art image processing methods prior to the search, such as Fisher-vector and C-SVC classifier, in order to semantically classify images containing multiple objects. The results of this offline classification are stored for the latter search task. We have elaborated more search methods for combining the re

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Lakshmi, D. Rajya. "Content based image retrieval using signature based similarity search." Indian Journal of Science and Technology 1, no. 5 (2008): 1–6. http://dx.doi.org/10.17485/ijst/2008/v1i5.2.

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LIN, HWEI-JEN, YANG-TA KAO, FU-WEN YANG, and PATRICK S. P. WANG. "CONTENT-BASED IMAGE RETRIEVAL TRAINED BY ADABOOST FOR MOBILE APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 04 (2006): 525–41. http://dx.doi.org/10.1142/s021800140600482x.

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This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by

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Wold, E., T. Blum, D. Keislar, and J. Wheaten. "Content-based classification, search, and retrieval of audio." IEEE Multimedia 3, no. 3 (1996): 27–36. http://dx.doi.org/10.1109/93.556537.

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Santosh, Kumar Swarnkar, and Avinash Sharma Prof. "Content Based Image Retrieval An Assessment." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 154–56. https://doi.org/10.31142/ijtsrd21708.

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The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems

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Shivanshu, Jaiswal, and Avinash Sharma Dr. "An Impact on Content Based Image Retrival A Perspective View." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 210–12. https://doi.org/10.5281/zenodo.3843069.

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The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems

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Chen, Qiang. "Research on Audio Retrieval Based on Content." Applied Mechanics and Materials 608-609 (October 2014): 304–8. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.304.

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This paper introduces the basic structure of audio retrieval system based on content, and in the related literature at home and abroad, analyzes the main features of audio retrieval algorithm that divided into the following several types: minimum distance method, neural network, Support Vector Machine, decision tree search algorithm and other audio retrieval algorithm. At the same time, this paper discusses some key techniques of audio retrieval.

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Shiral, J. V., Munmun Burman, Apurva Bhadbhade, Dhanashree Patil, Kajal Motghare, and Neha Wanjari. "Retrieval of Images Using SVM." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (2015): 106–11. http://dx.doi.org/10.53555/nncse.v2i3.500.

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Image retrieval is a technique which is used to search and retrieve images from a large database of digital images. Content-based image retrieval (CBIR) is a technique which allows searching images from large scale image database based on contents as needed by user.This paper introduces a technique to retrieve images by classifying it on the basis of the features and characteristics it contains using Support Vector Machine (SVM). The dataset of images is created which is used for feature matching purpose by SVM to find similar images from the database and based on user requirements images are

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Spaniol, Marc, Ralf Klamma, and Mathias Lux. "Imagesemantics: User-Generated Metadata, Content Based Retrieval & Beyond." JUCS - Journal of Universal Computer Science 14, no. (10) (2008): 1792–807. https://doi.org/10.3217/jucs-014-10-1792.

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With the advent of Web 2.0 technologies a new attitude towards processing contents in the Internet has emerged. Nowadays it is a lot easier to create, share and retrieve multimedia contents on the Web. However, with the increasing amount in contents retrieval becomes more challenging and often leads to inadequate search results. One main reason is that image clustering and retrieval approaches usually stick either solely to the images' low-level features or their user-generated tags (high-level features). However, this is frequently inappropriate since the "real" semantics of an image can only

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Chutel, Pushpa, Titiksha Bhagat, Snehal Dongre, and Sonam Chopade. "Content Based Reverse Image Search." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 01 SPL (2022): 1–5. http://dx.doi.org/10.18090/samriddhi.v14spli01.1.

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People can now get access to the required image with a relevant degree of information thanks to the broad improvement of the WWW. Details, photos, flow charts, logos, maps, and other information However, discovering and obtaining relevant information is always a challenge. There are certain text-based search engines, such as Google, that may be used to find desired photographs from the vast pool of images available on the internet. As a result, here need for the pictures online search engine that can search for related and proper images. Content-based image retrieval query approach is same as

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Kumar, Suneel, Manoj Kumar Singh, and Manoj Kumar Mishra. "Improve Content-based Image Retrieval using Deep learning model." Journal of Physics: Conference Series 2327, no. 1 (2022): 012028. http://dx.doi.org/10.1088/1742-6596/2327/1/012028.

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Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, fe

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Wan, Gary (Gang), and Zao Liu. "Content-Based Information Retrieval and Digital Libraries." Information Technology and Libraries 27, no. 1 (2008): 41. http://dx.doi.org/10.6017/ital.v27i1.3262.

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This paper discusses the applications and importance of content-based information retrieval technology in digital libraries. It generalizes the process and analyzes current examples in four areas of the technology. Content-based information retrieval has been shown to be an effective way to search for the type of multimedia documents that are increasingly stored in digital libraries. As a good complement to traditional textbased information retrieval technology, content-based information retrieval will be a significant trend for the development of digital libraries.

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Pokhrel, Sangita, Bina K C, and Prashant Bikram Shah. "A Practical Application of Retrieval-Augmented Generation for Website-Based Chatbots: Combining Web Scraping, Vectorization, and Semantic Search." Journal of Trends in Computer Science and Smart Technology 6, no. 4 (2025): 424–42. https://doi.org/10.36548/jtcsst.2024.4.007.

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The Retrieval-Augmented Generation (RAG) model significantly enhances the capabilities of large language models (LLMs) by integrating information retrieval with text generation, which is particularly relevant for applications requiring context-aware responses based on dynamic data sources. This research study presents a practical implementation of a RAG model personalized for a Chabot that answers user inquiries from various specific websites. The methodology encompasses several key steps: web scraping using BeautifulSoup to extract relevant content, text processing to segment this content int

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Joly, A., O. Buisson, and C. Frelicot. "Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search." IEEE Transactions on Multimedia 9, no. 2 (2007): 293–306. http://dx.doi.org/10.1109/tmm.2006.886278.

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Long, Xinwei, Zhiyuan Ma, Ermo Hua, Kaiyan Zhang, Biqing Qi, and Bowen Zhou. "Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24723–31. https://doi.org/10.1609/aaai.v39i23.34653.

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Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task. Current methods mainly employ separate retrieval and generation modules to acquire external knowledge and generate answers, respectively. We propose ReAuSE, an alternative to the previous RAG model for the knowledge-based VQA task, which seamlessly integrates knowledge retriever into the generative multi-modal large language model, serving as a built-in search engine. Specifically, our model functions both as a generative retriever and an accurate answer generator. It not o

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Rifiana, Arief, Widodo Suryarini, Bima Kurniawan Ary, Hustinawaty, and Arkan Faisal. "Advanced content-based retrieval for digital correspondence documents with ontology classification." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1665~1677. https://doi.org/10.11591/eei.v11i3.3376.

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using

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Anuradha, Shitole1 and Uma Godase2. "Survey on Content Based Image Retrieval." International Journal of Computer-Aided Technologies (IJCAx) 01, dec (2014): 01–09. https://doi.org/10.5281/zenodo.1450266.

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Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques

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Mawhesaran, T. "Document Vectorization for Large-Scale Information Retrieval Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41430.

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The invention presents an entirely new paradigm of document-search framework to make the retrieval and access of specific information from several thousands of documents, especially PDF files, as simple and easy as possible. The document content and metadata are retrieved for search using a vector-based approach with state-of-the-art vectorization techniques and vector databases such as MilvusDB. Document content is prepared and vectorized using preprocessing methods such as stop word removal, tokenization, normalization, etc. The vector data are stored in the database and indexed by such tech

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Gil, Ana B., Fernando de la Prieta, Sara Rodríguez, and Juan M. Corchado. "Smart System for the Retrieval of Digital Educational Content." Applied Sciences 9, no. 20 (2019): 4400. http://dx.doi.org/10.3390/app9204400.

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The education sector is a major generator, consumer, and depositary of educational content. Thanks to technological advances, today’s educators and learners have ubiquitous and on-demand access to information. Technology has made it possible for us to communicate and share information effortlessly from anywhere in the world. However, the availability of vast amounts of heterogeneous educational content will not be useful unless we search, retrieve and integrate it, creating interoperable educational environments. The current challenges to integrating educational content arise from its distribu

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Tseng, Chien-Hao, Chia-Chien Hsieh, Dah-Jing Jwo, Jyh-Horng Wu, Ruey-Kai Sheu, and Lun-Chi Chen. "Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation." Journal of Sensors 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/9566628.

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Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed perso

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Antani, S., Rodney Long, and T. M. Deserno. "Content-based Image Retrieval for Scientific Literature Access." Methods of Information in Medicine 48, no. 04 (2009): 371–80. http://dx.doi.org/10.3414/me0561.

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Summary Objectives: An increasing number of articles are published electronically in the scientific literature, but access is limited to alphanumerical search on title, author, or abstract, and may disregard numerous figures. In this paper, we estimate the benefits of using content-based image retrieval (CBIR) on article figures to augment traditional access to articles. Methods: We selected four high-impact journals from the Journal Citations Report (JCR) 2005. Figures were automatically extracted from the PDF article files, and manually classified on their content and number of sub-figure pa

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Rao, Thiriveedhi Yellamanda Srinivasa, and Pakanati Chenna Reddy. "Classification and Retrieval of Images Based on Extensive Context and Content Feature Set." Recent Patents on Computer Science 12, no. 3 (2019): 162–70. http://dx.doi.org/10.2174/2213275911666181107114537.

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Background: This paper renders a classification and retrieval of image achievements in the search area of image retrieval, especially content-based image retrieval, an area that has been very active and successful in the past few years. Objective: Primarily the features extracted established on the bag of visual words (BOW) can be arranged by utilizing Scaling Invariant Feature Transform (SIFT) and developed K-Means clustering method. Methods: The texture is extracted for a developed multi-texton method by our study. Our retrieval process consists of two stages such as retrieval and classifica

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Kafle, Anup, Anushil Timsina, Rochak Sedai, Sandeep Subedi, Upendra Prasad Neupane, and Prakash Chandra Prasad. "Content-Based Image Retrieval and Recommendation Based on User Reviews." International Journal on Engineering Technology 2, no. 1 (2024): 106–16. https://doi.org/10.3126/injet.v2i1.72526.

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Effective search functionality is crucial for enhancing user experience and boosting sales on e-commerce sites. However, relying on specific product names instead of common names can cause potential customers to be lost while searching for products, as many platforms still depend on text-based search engines. While text searches effectively find keywords, Search Engine fall short when customers do not know the exact product identity or only have an image of desired product. With the rise of multiple E-commerce sites, customers often feel confused about where to buy the best product. This paper

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Shivaditya, Jatar*1 &. Ayush Mittal2. "SURVEY AND DESIGN OF CONTENT BASED IMAGE RETRIEVAL USING DATA MINING CLUSTERING ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 160–63. https://doi.org/10.5281/zenodo.1002687.

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As processors become increasingly powerful, and memories become increasingly cheaper, the deployment of large image databases for a variety of applications have now become realizable. Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields for example, geography, medicine, architecture, advertising, design, fashion, and publishing. Effectively accessing desired images from large and varied image databases is now a necessity. Due to development of multimedia technology and increasing vogue of the computer network, the convent

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Haas, Werner, and Harald Mayer. "MPEG and its Relevance for Content-based Multimedia Retrieval." JUCS - Journal of Universal Computer Science 7, no. (6) (2001): 530–47. https://doi.org/10.3217/jucs-007-06-0530.

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The utilization of new emerging standards such as MPEG-7 is expected to be a major breakthrough for content-based multimedia data retrieval. The main features of the MPEG standards series and of related standards, formats and protocols are presented. It is discussed, how they, despite their partially early and immature stage, can best be utilized to yield effective results in the context of a knowledge management environment. Complementary to that, the current status and state of the art in content-based retrieval for images, video and audio content is briefly presented. In the context of the

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Guo, Min. "A Look into Content-Based Image Search Engine System." Advanced Materials Research 756-759 (September 2013): 1576–79. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1576.

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Image search engine technology is a combination of technologies of database, information retrieval, image processing and computer vision, texture recognition as well as multimedia database and network. This paper aims to present the flow chart of content-based image search engine system and explores the current image search technologies and its future development.

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Anish, L., and S. Thiyagarajan. "Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval." Indian Journal Of Science And Technology 17, no. 25 (2024): 2610–21. http://dx.doi.org/10.17485/ijst/v17i25.745.

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Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the S

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Shabnam, Kumari, Reema, and Kadian Yashika. "A Study on Content Based Image Retrieval." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 471–76. https://doi.org/10.31142/ijtsrd12930.

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The development of Internet causes an eruptive expansion of digital images, and also gives people more ways to get those images. Because the dissemination of video and image data in digital form has grown, Content Based Image Retrieval CBIR has become an eminent research topic. The importance of an effective technique in searching and retrieving images from the huge collection cannot be overemphasized. Therefore an important problem that needs to be addressed is fast retrieval of images from large databases. To perceive images that are perceptually similar to a query image, image retrieval sys

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Ungrangsi, Rachanee, Chutiporn Anutariya, and Vilas Wuwongse. "Enhancing Folksonomy-Based Content Retrieval with Semantic Web Technology." International Journal on Semantic Web and Information Systems 6, no. 1 (2010): 19–38. http://dx.doi.org/10.4018/jswis.2010010102.

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While Flickr, a widely-known photo sharing system, allows users to describe their own photos with tags (aka. folksonomy tags) for indexing purposes, its tag-based photo retrieval function is severely hampered by the inherent nature of folksonomy tags. This paper presents SemFlickr, an application which enhances the search in Flickr with its semantic query suggestion feature. SemFlickr employs SQORE, an ontology retrieval system, to retrieve relevant ontologies from the Semantic Web and then derives query term suggestions from those ontologies. To ensure that the highly related photos will appe

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Arief, Rifiana, Suryarini Widodo, Ary Bima Kurniawan, Hustinawaty Hustinawaty, and Faisal Arkan. "Advanced content-based retrieval for digital correspondence documents with ontology classification." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1665–77. http://dx.doi.org/10.11591/eei.v11i3.3376.

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using

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Ravi, G. "Content-Based Image Retrieval Using Deep Feature Extraction with ResNet-50." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48676.

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Abstract: This project presents a Content-Based Image Retrieval (CBIR) system that utilizes deep learning to improve the efficiency and accuracy of image similarity search. The system leverages a pre-trained ResNet-50 convolutional neural network, repurposed as a deep feature extractor by removing its final classification layers. Input images are first pre- processed and then passed through the network to extract high-dimensional feature vec- tors that capture rich visual semantics. These deep features are compared using cosine similarity to identify visually similar images. The system support

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Mang'are, F. Nyamisa, Mwangi Waweru, and Cheruiyot Wilson. "N-GRAM Based Semantic Enhanced Model for Product Information Retrieval." International Journal of Computer Science Issues 15, no. 2 (2018): 43–51. https://doi.org/10.5281/zenodo.1227767.

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The current information retrieval mechanisms are based on models such as Boolean model, extended Boolean model, vector space model, and probabilistic model and language models. However, these models fall short of expectations, leading to misunderstanding of the user query and therefore the information retrieved fail to meet user expectations. In this paper, a novel search technique is proposed as the possible solution to the problems inherent in the current information retrieval models. To achieve this objective, an experimental research design was utilized. This new technique is based on the

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Kamel, Abdelrahman, Youssef B. Mahdy, and Khaled F. Hussain. "Multi-Bin search: improved large-scale content-based image retrieval." International Journal of Multimedia Information Retrieval 4, no. 3 (2014): 205–16. http://dx.doi.org/10.1007/s13735-014-0061-0.

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Genest, David, and Michel Chein. "A content-search information retrieval process based on conceptual graphs." Knowledge and Information Systems 8, no. 3 (2004): 292–309. http://dx.doi.org/10.1007/s10115-004-0179-0.

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Chandy, D. Abraham, A. Hepzibah Christinal, Alwyn John Theodore, and S. Easter Selvan. "Neighbourhood search feature selection method for content-based mammogram retrieval." Medical & Biological Engineering & Computing 55, no. 3 (2016): 493–505. http://dx.doi.org/10.1007/s11517-016-1513-x.

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Mukhopadhyay, Mondrita, and Parthasarathi Mukhopadhyay. "From textual search to geodetic search: enhancing library retrieval systems." Indian Journal of Information Library & Society 36, no. 1-2 (2023): 10–21. https://doi.org/10.5281/zenodo.8222118.

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Information retrieval in libraries, regardless of their size or type, has primarily focused on textual search, neglecting the significant advancements in digital information representation and retrieval such as image search, multimedia retrieval, multilingual retrieval, integrated discovery search, and other related areas like geodetic search. Geodetic search refers to a type of search functionality that incorporates geographic coordinates and spatial data in information retrieval systems. It utilizes coordinates, such as latitude and longitude, to identify and retrieve relevant data associate

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Segeda, Oleksii. "Building Intelligent Search Systems: Advances in AI-Based Information Retrieval." American Journal of Applied Sciences 07, no. 06 (2025): 06–11. https://doi.org/10.37547/tajas/volume07issue06-02.

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The exponential growth of digital content has driven the need for more intelligent, context-aware information retrieval systems. While traditional keyword-based search engines remain foundational, they often fall short of capturing deeper semantic meaning. This article explores the evolution, methodologies, and recent developments in intelligent information retrieval systems powered by artificial intelligence. Special attention is given to the use of machine learning, natural language processing (NLP), and neural networks to improve relevance, personalization, and contextual understanding, inc

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