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Journal articles on the topic 'Multimedia information retrieval system'

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Published: 5 June 2025

Last updated: 15 July 2025

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Davcev, Danco, Dusan Cakmakov, and Vanco Cabukovski. "Distributed multimedia information retrieval system." Computer Communications 15, no. 3 (1992): 177–84. http://dx.doi.org/10.1016/0140-3664(92)90078-s.

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Maha, Mahmood, Jaber AL-kubaisy Wijdan, and Al-Khateeb Belal. "Multimedia information retrieval using artificial neural network." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 146–54. https://doi.org/10.11591/ijai.v12.i1.pp146-154.

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The importance of the multimedia information retrieval (MIR) is highlighted by the extensive amount of the information on the internet. Image, audio, video, and text are all examples of the characteristics of the raw multimedia data. It is greatly challenging to represent a concept of human perception and how the machine-level language can grasp it (semantic gap of MIR). However, this paper aims to improve the information retrieval model that retrieves data from multimedia. This can be implemented by leveraging the use of variety of algorithms that go through training and testing to extract the model. One of these algorithms extracts text information based on the query language's nature as the vector space model (VSM) and the latent semantic index (LSI) were used. The other technique uses curvelet decomposition and statistic parameters like mean, standard deviation, and signal energy to recover these properties. Additionally, a discrete wavelet transforms (DWT) and signal characteristics-based method is used to retrieve audio signals. Finally, the neural network learning is modeled and trained on a collection of different multimedia images. The learned features have been utilized for presenting a highly sufficient system of multimedia retrieval which operates for a large set of multi-modal datasets.

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Mahmood, Maha, Wijdan Jaber AL-kubaisy, and Belal Al-Khateeb. "Multimedia information retrieval using artificial neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 146. http://dx.doi.org/10.11591/ijai.v12.i1.pp146-154.

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<span lang="EN-US">The importance of the multimedia information retrieval (MIR) is highlighted by the extensive amount of the information on the internet. Image, audio, video, and text are all examples of the characteristics of the raw multimedia data. It is greatly challenging to represent a concept of human perception and how the machine-level language can grasp it (semantic gap of MIR). However, this paper aims to improve the information retrieval model that retrieves data from multimedia. This can be implemented by leveraging the use of variety of algorithms that go through training and testing to extract the model. One of these algorithms extracts text information based on the query language's nature as the vector space model (VSM) and the latent semantic index (LSI) were used. The other technique uses curvelet decomposition and statistic parameters like mean, standard deviation, and signal energy to recover these properties. Additionally, a discrete wavelet transforms (DWT) and signal characteristics-based method is used to retrieve audio signals. Finally, the neural network learning is modeled and trained on a collection of different multimedia images. The learned features have been utilized for presenting a highly sufficient system of multimedia retrieval which operates for a large set of multi-modal datasets. </span>

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van Zwol, Roelof, Stefan Rüger, Mark Sanderson, and Yosi Mass. "Multimedia information retrieval." ACM SIGIR Forum 41, no. 2 (2007): 77–82. http://dx.doi.org/10.1145/1328964.1328978.

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Manmatha, R., Stefan Rüger, and Alex Hauptmann. "Multimedia information retrieval." ACM SIGIR Forum 39, no. 2 (2005): 40–41. http://dx.doi.org/10.1145/1113343.1113352.

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Chang, Shi-Kuo, Daniel Graupe, Keiko Hasegawa, and Hubert Kordylewski. "An Active Multimedia Information System for Information Retrieval, Discovery and Fusion." International Journal of Software Engineering and Knowledge Engineering 08, no. 01 (1998): 139–60. http://dx.doi.org/10.1142/s0218194098000108.

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To accomplish the retrieval, discovery and fusion of multimedia information from diverse sources, an active multimedia information system capable of retrieving, processing and filtering multimedia information, checking for consistency, and structuring the relevant information for distribution is needed. We describe a framework for the human- and system-directed retrieval, discovery and fusion of multimedia information, which is based upon the observation that a significant event often manifests itself in different media over time and space. Therefore if we can index such manifestations and dynamically link them, then we can check for consistency and discover important and relevant multimedia information. This dynamic indexing technique is based upon the theory of active index. For the discovery of significant events, a powerful newly developed artificial neural network is used to serve as the decision network subsystem of the proposed information system. An experimental system is implemented for further empirical research.

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Bordogna, G., P. Carrara, I. Gagliardi, D. Merelli, F. Naldi, and M. Padula. "A system architecture for multimedia information retrieval." Journal of Studies in International Education 16, no. 4 (1990): 229–38. http://dx.doi.org/10.1177/102831539001600403.

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Bordogna, G., P. Carrara, I. Gagliardi, D. Merelli, F. Naldi, and M. Padula. "A system architecture for multimedia information retrieval." Journal of Information Science 16, no. 4 (1990): 229–38. http://dx.doi.org/10.1177/016555159001600403.

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Xie, Dan, and Chao Yin. "Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology." PeerJ Computer Science 9 (April 18, 2023): e1330. http://dx.doi.org/10.7717/peerj-cs.1330.

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Image retrieval technology has emerged as a popular research area of China’s development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network’s capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system’s picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.

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Meghini, Carlo, Fabrizio Sebastiani, and Umberto Straccia. "A model of multimedia information retrieval." Journal of the ACM 48, no. 5 (2001): 909–70. http://dx.doi.org/10.1145/502102.502103.

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Jena, Gouranga Charan, and Siddharth Swarup Rautaray. "A comprehensive survey on cross-language information retrieval system." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 127. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp127-134.

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Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, Cross language information retrieval system, are nowadays in strong demand. CLIR is a subset of Information Retrieval (IR) system. Information Retrieval deals with finding useful information from a large collection of unstructured, structured and semi-structured data to a user query where the query is a set of keywords. Information Retrieval can be classified into different classes such as Monolingual information retrieval, Bi-Lingual Information Retrieval, Multilingual information retrieval and Cross language information retrieval. This paper focuses on the various IR variants and techniques used in CLIR system. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. It gives an overview of the advantages, limitations, tools available in CLIR research. It also describes new application areas of CLIR such as medical, multimedia, question answering system etc. The need for exploring and building more specialized information system that enable speakers of an Odia language to discover valuable information beyond linguistic and cultural barriers. This study is aimed at building an experimental CLIR system between one of the under-resourced language (i.e. Odia) and one of the most commonly used online language (i.e. English) in future.

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Lew, Michael S. "Best papers in multimedia information retrieval." International Journal of Multimedia Information Retrieval 2, no. 2 (2013): 71–72. http://dx.doi.org/10.1007/s13735-013-0035-7.

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Djeraba, Chabane, Nicu Sebe, and Michael S. Lew. "Systems and architectures for multimedia information retrieval." Multimedia Systems 10, no. 6 (2005): 457–63. http://dx.doi.org/10.1007/s00530-005-0174-7.

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Srihari, Rohini K., Zhongfei Zhang, R. Manmatha, and Chandu Ravela. "Multimedia indexing and retrieval." ACM SIGIR Forum 32, no. 2 (1998): 29–30. http://dx.doi.org/10.1145/305110.305130.

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Srihari, Rohini K., Zhongfei Zhang, R. Manmatha, and Chandu Ravela. "Multimedia indexing and retrieval." ACM SIGIR Forum 33, no. 1 (1999): 34–35. http://dx.doi.org/10.1145/331403.331412.

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Wang, Zhongke. "Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm." Advances in Multimedia 2021 (December 16, 2021): 1–7. http://dx.doi.org/10.1155/2021/2630254.

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This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out that the content-based multimedia retrieval technology is bound to be organically combined with the traditional text retrieval methods. The information retrieval technologies in the past can only comply with the specific requirements of customers. Due to their characteristics of universality, they can hardly meet the demands of different environments, various purposes, and different times at the same time yet. Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. Subsequently, the corresponding calculations are carried out, and the model is updated accordingly. Through the experiments, it is verified that the probability model put forward in this paper is the optimal solution to express the interest of customers and its changes.

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MORE, MAHADEV A. "CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25835.

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CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.

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Wagenpfeil, Stefan, Felix Engel, Paul Mc Kevitt, and Matthias Hemmje. "AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones." Information 12, no. 1 (2021): 43. http://dx.doi.org/10.3390/info12010043.

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To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

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Zhang, Hao, Gong Wen Xu, Wan Rong Guo, et al. "The Application of Cross-Media Retrieval Technology Based on Ontology." Applied Mechanics and Materials 738-739 (March 2015): 1299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1299.

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As a large number of the multimedia information emerges, the cross-media retrieval system becomes an important research focus. The cross-media retrieval system is based on the traditional content retrieval, extracting color, texture, and shape features vector of the images. A new method was carried out in this paper. Firstly, the uniform semantic representational framework was built to organize the different mode media heterogeneous characteristics. Secondly, the Ontology database representing each type of media concepts was set up. The Ontology database organizes the low level features of the multimedia objects to associate multimedia files in the semantic level. Thirdly, the cross-media retrieval algorithm based on ontology was introduced. The results of the experiment showed that this cross-media retrieval method based on the Ontology was more effective and accurate.

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Huan, Junrong. "Research on the Application of Artificial Intelligence in Image and Text Database Retrieval." Frontiers in Computing and Intelligent Systems 2, no. 1 (2022): 39–41. http://dx.doi.org/10.54097/fcis.v2i1.2708.

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In the graphic database, the difficulty of query processing lies in how to query the contents of various data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. In graphic database, the difficulty of query processing lies in how to query the content based on unformatted data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. Intelligent information retrieval (IR) system is an intelligent computer IR system, which simulates the thinking process and intelligent activities of human beings about information processing, realizes the storage, retrieval and reasoning of information knowledge, and provides intelligent assistance to users. This paper analyzes the problems of image and text database retrieval driven by big data, explores the application effect of artificial intelligence (AI) in IR driven by big data, promotes the innovation and transformation of modern science and technology, and realizes the sustainable development of our society.

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Davis, G. L., Edward F. Gilman, and Howard W. Beck. "An Electronically Based Horticultural Information Retrieval System." HortTechnology 6, no. 4 (1996): 332–36. http://dx.doi.org/10.21273/horttech.6.4.322.

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A large horticultural database and an electronic retrieval system for extension education programs were developed using compact disk-read only memory (CD-ROM) and World Wide Web (WWW) as the medium for information delivery. Object-oriented database techniques were used to organize the information. Conventional retrieval techniques including hypertext, full text searching, and expert systems were integrated into a complete package for accessing information stored in the database. A multimedia user interface was developed to provide a variety of capabilities including computer graphics and high resolution digitized images. Information for the CD-ROM was gathered from extension publications that were tagged using the standard generalized markup language (SGML)-based document markup language (International Standards Organization, 1986). Combining funds from the state legislator with grants from the USDA and other institutions, the CD-ROM system has been implemented in all 67 county extension offices in Florida and is available to the public as a for-sale CD-ROM. Public access is also available to most of the database through the WWW.

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Smith, Roger W., Dorota Kieronska, and Svetha Venkatesh. "Conceptual Representation for Multimedia Information." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 02 (1997): 303–27. http://dx.doi.org/10.1142/s0218001497000147.

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Multimedia information is now routinely available in the forms of text, pictures, animation and sound. Although text objects are relatively easy to deal with (in terms of information search and retrieval), other information bearing objects (such as sound, images, animation) are more difficult to index. Our research is aimed at developing better ways of representing multimedia objects by using a conceptual representation based on Schank's conceptual dependencies. Moreover, the representation allows for users' individual interpretations to be embedded in the system. This will alleviate the problems associated with traditional semantic networks by allowing for coexistence of multiple views of the same information. The viability of the approach is tested, and the preliminary results reported.

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Bondarenko, Bogdan, and Yuri Samokhvalov. "SEARCH FOR MULTIMEDIA INFORMATION BASED ON NEURAL NETWORKS." Information systems and technologies security, no. 1 (3-4) (2020): 57–62. http://dx.doi.org/10.17721/ists.2020.4.58-62.

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The article considers approaches to the use of neural networks in multimedia information retrieval. The develop ment of methods for multimedia information retrieval is necessary due to the large amount of such information. Tradi tional methods of multimedia information retrieval have a high speed of data processing, but low accuracy due to the inability of semantic search. The use of neural networks allows for semantic search, which increases its accuracy and completeness. Approaches to the use of neural networks at the stages of indexing and retrieval of multimedia infor mation are considered. With the help of a neural network, a multimedia file is analyzed and classified. The result of classifying a file is used to create its textual description - an annotation that is compared to the search query to deter mine relevance. There are many ready-made classification networks that can be used to speed up the process of creat ing a multimedia search system, but it is not possible to create a neural network to classify all real-world objects, so multiple neural networks should be used. Neural networks are also use to build feature vectors for a media file and a search query. Similarity functions, such as cosine of similarity, are applied to constructed vectors to determine the semantic similarity of a query and a media file. In this case, the search query can be both in text form and in the form of the appropriate format of the desired media file. This approach allows to build an optimal neural network for a specific task. Neural networks are used to compare the constructed annotation of a file and a query, which increases the accu racy and completeness of the search, compared to traditional methods, due to the ability of neural networks to take into account the semantic meaning of the text.

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T., Dr Vijayakumar, and Vinothkanna R. "RETRIEVAL OF COMPLEX IMAGES USING VISUAL SALIENCY GUIDED COGNITIVE CLASSIFICATION." Journal of Innovative Image Processing 2, no. 2 (2020): 102–9. http://dx.doi.org/10.36548/jiip.2020.2.005.

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Data storage via multimedia technology is more preferred as the information in multimedia contain rich meanings and are concise when compared to the traditional textual information. However, efficient information retrieval is a crucial factor in such storage. This paper presents a cognitive classification based visual saliency guided model for the efficient retrieval of information from multimedia data storage. The Itti visual saliency model is described here for generation of an overall saliency map with the integration of color saliency, intensity and direction maps. Multi-feature fusion paradigms are used for providing clear description of the image pattern. The definition is based on two stages namely complexity based on cognitive load and classification of complexity at a cognitive level. The image retrieval system is finalized by integrating a group sparse logistic regression model. In complex scenarios, the baselines are overcome by the proposed system when tested on multiple databased as compared to other state-of-the-art models.

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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 Farradane's work. The basic proposal has been extended to deal with specific requirements of the considered application and to be automatically performed. A first prototype of a multimedia information retrieval system has been implemented with the goal of validating the method in the specific application.

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Zhao, Meng Ling, and Xiao Li Tang. "Design of Application Management Software of Multimedia Rescue Communication System." Advanced Materials Research 760-762 (September 2013): 911–15. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.911.

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In view of problems of limited information, dispersed storage data and single function, the paper proposed a design scheme of application management software of multimedia rescue communication system, which can coordinate with front-end device of multimedia rescue communication system to process and store rescue information real-timely. It introduced design of function modules of the software in details through analyzing design requirements of the software and gave implementing principles of technologies of multimedia information fusion and information retrieval used in the software. The actual application showed that the software has functions of video display and storage, multiparty calls, environmental parameters display and alarming, which meets with requirements of diversified information and rescue information sharing of rescue work.

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Westerveld, Thijs, and Roelof van Zwol. "Multimedia retrieval at INEX 2006." ACM SIGIR Forum 41, no. 1 (2007): 58–63. http://dx.doi.org/10.1145/1273221.1273226.

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Tsikrika, Theodora, and Thijs Westerveld. "Multimedia retrieval at INEX 2007." ACM SIGIR Forum 42, no. 1 (2008): 16–21. http://dx.doi.org/10.1145/1394251.1394254.

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Uma, R., and K. Muneeswaran. "OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining." Cybernetics and Systems 48, no. 4 (2017): 393–414. http://dx.doi.org/10.1080/01969722.2017.1285163.

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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, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).

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Steinert, Patrick, Stefan Wagenpfeil, Paul Mc Kevitt, Ingo Frommholz, and Matthias Hemmje. "Parallelization Strategies for Graph-Code-Based Similarity Search." Big Data and Cognitive Computing 7, no. 2 (2023): 70. http://dx.doi.org/10.3390/bdcc7020070.

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The volume of multimedia assets in collections is growing exponentially, and the retrieval of information is becoming more complex. The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. Feature graphs contain semantic information on multimedia assets. Machine learning can produce detailed semantic information on multimedia assets, reflected in a high volume of nodes and edges in the feature graphs. While increasing the effectiveness of the information retrieval results, the high level of detail and also the growing collections increase the processing time. Addressing this problem, Multimedia Feature Graphs (MMFGs) and Graph Codes (GCs) have been proven to be fast and effective structures for information retrieval. However, the huge volume of data requires more processing time. As Graph Code algorithms were designed to be parallelizable, different paths of parallelization can be employed to prove or evaluate the scalability options of Graph Code processing. These include horizontal and vertical scaling with the use of Graphic Processing Units (GPUs), Multicore Central Processing Units (CPUs), and distributed computing. In this paper, we show how different parallelization strategies based on Graph Codes can be combined to provide a significant improvement in efficiency. Our modeling work shows excellent scalability with a theoretical speedup of 16,711 on a top-of-the-line Nvidia H100 GPU with 16,896 cores. Our experiments with a mediocre GPU show that a speedup of 225 can be achieved and give credence to the theoretical speedup. Thus, Graph Codes provide fast and effective multimedia indexing and retrieval, even in billion-scale use cases.

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Pham, Nhut Minh, Hieu Quang Pham, Thi Hieu Luong, and Quan Hai Vu. "Hybrid operations for content-based Vietnamese agricultural multimedia information retrieval." Science and Technology Development Journal 18, no. 4 (2015): 51–63. http://dx.doi.org/10.32508/stdj.v18i4.909.

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Content-based multimedia information retrieval is never a trivial task even with state-of-the-art approaches. Its mandatory challenge, called “semantic gap,” requires much more understanding of the way human perceive things (i.e., visual and auditory information). Computer scientists have spent thousands of hours seeking optimal solutions, only ended up falling in the bound of this gap for both visual and spoken contexts. While an over-the-gap approach is unreachable, we insist on assembling current viable techniques from both contexts, aligned with a domain concept base (i.e., an ontology), to construct an info service for the retrieval of agricultural multimedia information. The development process spans over three packages: (1) building a Vietnamese agricultural thesaurus; (2) crafting a visual-auditory intertwined search engine; and (3) system deployment as an info service. We spring our the thesaurus in 2 sub-boughs: the aquaculture ontology consists of 3455 concepts and 5396 terms, with 28 relationships, covering about 2200 fish species and their related terms; and the plant production ontology comprises of 3437 concepts and 6874 terms, with 5 relationships, covering farming, plant production, pests, etc. These ontologies serve as a global linkage between keywords, visual, and spoken features, as well as providing the reinforcement for the system performances (e.g., through query expansion, knowledge indexing…). On the other hand, constructing a visual-auditory intertwined search engine is a bit trickier. Automatic transcriptions of audio channels are marked as the anchor points for the collection of visual features. These features, in turn, got clustered based on the referenced thesauri, and ultimately tracking out missing info induced by the speech recognizer’s word error rates. This compensation technique bought us back 14 % of loss recall and an increase of 9 % accuracy over the baseline system. Finally, wrapping the retrieval system as an info service guarantees its practical deployment, asour target audiences are the majority of farmers in developing countries who are unable to reach modern farming information and knowledge.

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Zhang, Wenwen. "Classification and Retrieval of Multimedia Audio Learning Resources." International Journal of Emerging Technologies in Learning (iJET) 18, no. 20 (2023): 99–113. http://dx.doi.org/10.3991/ijet.v18i20.44221.

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With the development of the Internet and new media, multimedia and audio learning resources have been widely used in teaching and learning. However, their classification and retrieval have become important and urgent issues to be addressed. This study conducted in-depth research on the classification system, construction, and retrieval of multimedia audio learning resources, with the aim of solving several problems with existing research methods, such as timeconsuming manual labeling, inconsistent labeling, and traditional retrieval methods neglecting the correlation between audio and metadata. First, a classification model of audio learning resources was constructed. It processed single-mode data from audios and annotated texts and further abstracted the single-mode information into high-level feature vectors. Then the complementarity between multi-modalities was used to fuse the abstract features or decisionmaking results and eliminate information redundancy between modalities, thereby learning a better feature representation of multimedia audio learning resources. Second, a retrieval method for the resources based on self-similarity matrix filtering was proposed, which aimed to improve the accuracy and efficiency of retrieval. This study provides a new theoretical and practical perspective for classifying and retrieving multimedia audio learning resources.

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Mahajan, Arpana, and Sanjay Chaudhary. "Study of Different Features and Classifiers for Image Retrieval." International Journal of Engineering & Technology 7, no. 4.19 (2018): 58–62. http://dx.doi.org/10.14419/ijet.v7i4.19.22015.

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With the advent of multimedia and imaging technology, lots ofÂ Â images sharing and uploading over the internet have been increased. It instigated development of potential image retrieval system to satisfy the requirement of mankind. The content-based image retrieval (CBIR) system retrieves the desired image by low level features similar to color, shape and texture which are not enough to explain the userâ€™s high level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the concepts in image retrieval are keywords, conditions or text. Conditions are used by human to explain their information need and it also used by system as a way to stand for images. Here in this paper different types of features their advantage and disadvantages are described.Â Â

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Celentano, Augusto, Ombretta Gaggi, and Maria Luisa Sapino. "Retrieval in multimedia presentations." Multimedia Systems 10, no. 1 (2004): 72–82. http://dx.doi.org/10.1007/s00530-004-0138-3.

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Lew, Michael S. "Multimedia information retrieval: best papers and expanding frontiers." International Journal of Multimedia Information Retrieval 3, no. 2 (2014): 67–68. http://dx.doi.org/10.1007/s13735-014-0054-z.

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Ni, Tong. "An Intelligent Retrieval Algorithm for Digital Literature Promotion Information Based on TRS Information Retrieval." International Journal of Information Technologies and Systems Approach 16, no. 2 (2023): 1–14. http://dx.doi.org/10.4018/ijitsa.318458.

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The world has entered the information age, and one of the main factors of social development is various information resources. Word processing is one of the first information technologies to be developed that has grown very rapidly and successfully. As a category of multimedia resources, documents are widely used in institutions, such as corporations, governments, and digital libraries. Traditional search technology has been unable to meet the needs of this development, and how to find information in this sea of digital information has become an urgent problem. Therefore, this study has conducted a research experiment on promoting digital literature for text retrieval system information retrieval. The experimental data have shown that 310 (73.81%) students wanted WeChat and Weibo as a way of digital literature retrieval and promotion. Moreover, 267 (63.57%) students wanted e-books as a way to promote, and 172 (40.95%) students would like to participate in novel digital literature retrieval promotion activities.

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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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Carrara, P., A. Della Ventura, and I. Gagliardi. "Designing hypermedia information retrieval systems for multimedia art catalogues." New Review of Hypermedia and Multimedia 2, no. 1 (1996): 175–95. http://dx.doi.org/10.1080/13614569608914680.

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Golshani, F., and N. Dimitrova. "Retrieval and delivery of information in multimedia database systems." Information and Software Technology 36, no. 4 (1994): 235–42. http://dx.doi.org/10.1016/0950-5849(94)90077-9.

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Lu, Guojun. "Design issues of multimedia information indexing and retrieval systems." Journal of Network and Computer Applications 22, no. 3 (1999): 175–98. http://dx.doi.org/10.1006/jnca.1999.0090.

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Qi, Aili, Yunsong Wang, and Chengchun Shen. "Application of Courseware Based on Information Retrieval Technology." International Journal of Emerging Technologies in Learning (iJET) 11, no. 03 (2016): 32. http://dx.doi.org/10.3991/ijet.v11i03.5346.

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distance teaching has become one of important approaches for people to acquire knowledge. However, due to time limit, it is very difficult for people to spare much time from work and life on distance teaching. Besides, for numerous employees, they have mastered some knowledge in their fields, so it is unnecessary to complete all courses of distance teaching. Thus, retrieval knowledge acquisition becomes very crucial. The author proposed a courseware teaching system with retrieval function which effectively solves courseware retrieval based on multimedia information such as image, audit and video. The author applied this system in actual teaching of “Sports Psychology” course, gained favorable teaching effect, and improved students’ learning efficiency and learning effect as well as their initiative and enthusiasm for acquiring knowledge.

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Thanh, Van The, Do Quang Khoi, Le Huu Ha, and Le Manh Thanh. "SIR-DL: AN ARCHITECTURE OF SEMANTIC-BASED IMAGE RETRIEVAL USING DEEP LEARNING TECHNIQUE AND RDF TRIPLE LANGUAGE." Journal of Computer Science and Cybernetics 35, no. 1 (2019): 39–56. http://dx.doi.org/10.15625/1813-9663/35/1/13097.

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The problem of finding and identifying semantics of images is applied in multimedia applications of many different fields such as Hospital Information System, Geographic Information System, Digital Library System, etc. In this paper, we propose the semantic-based image retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents. At the same time we identify the semantics of similar images on Ontology, which describes semantics of visual features of images. Firstly, the color and spatial features of segmented images are we extracted and these visual feature vectors are trained on the deep neural network to obtain visual words vectors. The process of image retrieval is executed rely on semantic classification of SIR-DL according to the visual feature vector of the query image from which it produces a visual word vector. Then, we retrieve it on Ontology to provide the identities and the semantics of similar images corresponds to a similarity measure. In order to carry out SIR-DL, the algorithms and diagram of this image retrieval system are proposed after that we implement them on ImageCLEF@IAPR, which has 20,000 images. On the base of the experimental results, the effectiveness of our method is evaluated by the accuracy, precision, recall, and F-measure; these results are compared with some of works recently published on the same image dataset. It shows that SIR-DL effectively solves the problem of semantic-based image retrieval and can be used to build multimedia systems in many different fields.

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Pan, Hong, and Yi Yang. "Combining location and feature information for multimedia retrieval." International Journal of Computer Applications in Technology 38, no. 1/2/3 (2010): 27. http://dx.doi.org/10.1504/ijcat.2010.034136.

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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 associated with particular places or regions and can be particularly useful in domains where location plays a significant role in retrieval, such as documentary resources on geography, geology, environmental sciences, urban planning, and travel. This paper aims to augment the information retrieval capabilities of a typical library retrieval system by integrating geodetic search functionalities. The proposed prototype framework utilizes VuFind, an open-source library discovery software based on Solr, as the retrieval system. It incorporates Leaflet, an open-source JavaScript library for interactive maps, and OpenStreetMap as the cartographic data provider, which is available under the Open Data Commons Open Database License (ODbL). The prototype deploys the indexing of coordinate data (longitude, latitude and bounding box) for a given set of MARC records using the tag 034, subfields d, e, f, g, and h (specific to different place names of India) in Koha, and then extends the geodetic search facility in VuFind by harvesting indexed MARC records from Koha.

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Lu, Yu, and Wang Lizhi. "Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm." Scientific Programming 2021 (December 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/9948800.

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In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.

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Mohan, Prakash, Balasaravanan Kuppuraj, and Saravanakumar Chellai. "An Enhanced Security Measure for Multimedia Images Using Hadoop Cluster." International Journal of Operations Research and Information Systems 12, no. 3 (2021): 1–7. http://dx.doi.org/10.4018/ijoris.20210701.oa4.

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Information are generated over the internet for every second. These information are not fully secured. To increase the security of these information send over the internet there are two methods Cryptography and Steganography are combined to encrypt the data using RSA algorithm as well as to hide the data in multimedia image in Hadoop Cluster. Features of the resultant image such as color are extracted and stored separately in Hadoop cluster to enhance security. Then combining features of the Stenographic image for secret image retrieval, which has been then split into image and secret information. At last, decrypting the secret information, we retrieve the actual information. Application of this system in Hadoop will increase the speed of execution of the process.

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Aouadi, Hatem, Mouna Torjmen-Khemakhem, and Maher Ben Jemaa. "Combination of document structure and links for multimedia object retrieval." Journal of Information Science 38, no. 5 (2012): 442–58. http://dx.doi.org/10.1177/0165551512445851.

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In this paper, we are interested in XML multimedia retrieval, the aim of which is to find relevant multimedia objects such as images, audio and video through their context as document structure. In context-based multimedia retrieval, the most common technique is based on the text surrounding the image. However, such textual information can be irrelevant to the image content. Therefore many works are oriented to the use of alternative techniques to extend the image description, such as the use of ontologies, relevance feedback, and user profiles. We studied in our work the use of links between XML elements to improve image retrieval. More precisely, we propose dividing the document into regions through the document structure and image position. Then we weight links between these regions according to their hierarchical positions, in order to distinguish between links that are useful and those that are not useful. We then apply an updated version of the HITS algorithm at the region level, and compute a final image score by combining link scores with initial image scores. Experiments were done on the INEX 2006 and 2007 multimedia tracks, and showed the potential of our method.

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Yager, Ronald R. "Fuzzy Temporal Methods for Video Multimedia Information Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (1997): 37–44. http://dx.doi.org/10.20965/jaciii.1997.p0037.

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We consider here the issue of querying video multimedia information systems. Central to this problem is the ability to specify and represent various natural temporal concepts used in the formulation of queries posed by the users of these systems. It is suggested that fuzzy set technology provides a very useful formalism for representing these temporal concepts. We then investigate various aspects of the use of fuzzy set methods for the retrieval of information in annotated video multimedia systems.

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Bilquees, Samina, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ali Javed, and Muhammad Tariq Mahmood. "Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval." International Journal of Optics 2021 (July 14, 2021): 1–19. http://dx.doi.org/10.1155/2021/4151482.

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In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

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