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Journal articles on the topic 'Content-based information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Lew, Michael S., Nicu Sebe, Chabane Djeraba, and Ramesh Jain. "Content-based multimedia information retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 2, no. 1 (February 2006): 1–19. http://dx.doi.org/10.1145/1126004.1126005.

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S ,, Madhusudhan, Channakeshava K.R ,, and Dr T. Rangaswamy. "Content-Based Image Retrieval System for Optical Fiber Sensor Information Processing." International Journal of Engineering Research 3, no. 6 (June 1, 2014): 398–401. http://dx.doi.org/10.17950/ijer/v3s6/607.

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Varma, Ankitha, and Dr Kamalpreet Kaur. "Survey on content based image retrieval." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 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 of extracting desired information from the retrieved images. This paper pre- sents a systematic and a detailed review of the CBIR method along with the different databases and evaluation parameters used for the analysis. An attempt has been made to include an exhaustive literature survey of the various CBIR approaches.

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FENG, DAGAN (DAVID). "CONTENT-BASED RETRIEVAL OF MULTIMEDIA INFORMATION." International Journal of Image and Graphics 01, no. 01 (January 2001): 83–91. http://dx.doi.org/10.1142/s0219467801000074.

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The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.

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Lau, B. T., and Y. C. Wang. "CONTENT-BASED SPATIAL QUERY RETRIEVAL." Journal of IT in Asia 2, no. 1 (April 26, 2016): 53–82. http://dx.doi.org/10.33736/jita.55.2007.

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Information acquisition with the availability of modern information technology has become easier. We rely on various information systems in our daily lives. Geographical information system and spatial query retrieval become more and more important in vehicle navigation, robot automation, and satellite signal processing. Spatial query is made easy with the handheld technology like PDA and sketching device. However powerful query methodology needs powerful retrieval techniques to produce the desired output. Content based spatial query retrieval is one of the best resorts for spatial query retrieval. Structural spatial query retrieval is in content based retrieval family that is also an active research area in spatial databases. Structural spatial query retrieval assesses similarity by its structural arrangement, known as configuration similarity. This research developed an enhanced structural spatial query retrieval model for spatial databases.

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Wan, Gary (Gang), and Zao Liu. "Content-Based Information Retrieval and Digital Libraries." Information Technology and Libraries 27, no. 1 (March 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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Hanmandlu, Madasu, and Anirban Das. "Content-based Image Retrieval by Information Theoretic Measure." Defence Science Journal 61, no. 5 (September 2, 2011): 415. http://dx.doi.org/10.14429/dsj.61.1177.

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<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>

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Swathi, J., and G. Seethalakshmi. "Content and Location based Information Retrieval System." International Journal of Computer Applications 107, no. 11 (December 18, 2014): 1–4. http://dx.doi.org/10.5120/18792-0115.

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Cannavale, F., A. Casanova, M. Fraschini, and V. Savona. "Content image retrieval based on topological information." Journal of Visual Languages & Computing 15, no. 5 (October 2004): 347–59. http://dx.doi.org/10.1016/j.jvlc.2003.11.003.

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Lippincott, Aura. "Issues in content-based music information retrieval." Journal of Information Science 28, no. 2 (April 2002): 137–42. http://dx.doi.org/10.1177/016555150202800205.

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IKEDA, TAKASHI, and MASAFUMI HAGIWARA. "CONTENT-BASED IMAGE RETRIEVAL SYSTEM USING NEURAL NETWORKS." International Journal of Neural Systems 10, no. 05 (October 2000): 417–24. http://dx.doi.org/10.1142/s0129065700000326.

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An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.

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Turner, David D., and Ulrich Löhnert. "Ground-based temperature and humidity profiling: combining active and passive remote sensors." Atmospheric Measurement Techniques 14, no. 4 (April 26, 2021): 3033–48. http://dx.doi.org/10.5194/amt-14-3033-2021.

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Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.

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Song, Ya Hui, Xiao Chen, and Shan Shan Qu. "Content Based Image Retrieval with Color Invariants." Advanced Materials Research 760-762 (September 2013): 1604–8. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1604.

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Content based image retrieval (CBIR) is an essential task in many applications. Color based methods have received much attention in past years, since color could serve efficiently for image retrieval, especially in the case of large database. However, there are two main drawbacks for color based image retrieval methods. Firstly, color based methods are not suitable for similar scenes under different illumination conditions, because color is sensitive to illumination. Secondly, existing approaches usually employ image descriptors with large size, which makes the approach unsuitable for real-time application. To overcome drawbacks mentioned above, an adaptive image retrieval method has been proposed, which integrates the color invariant with the spatial information about images. Different from previous methods, the quantization of the color space has not been manually determined. Instead, it has been decided according to the content of image, using an adaptive clustering technique. Therefore, the size of image descriptor is very small. In the proposed method, feature maps for images have been firstly established, which consist of color invariants. And then the Markov chain model has been employed to capture color information and spatial features. Finally, similar images are retrieved based on two-stage weighted distance. Experimental results show that the proposed method has improved simplicity and compactness of color based image retrieval methods, without the loss of efficiency and robustness.

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LIN, RUEI-SHIANG, and LING-HWEI CHEN. "CONTENT-BASED AUDIO RETRIEVAL BASED ON GABOR WAVELET FILTERING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 06 (September 2005): 823–37. http://dx.doi.org/10.1142/s0218001405004290.

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Rapid increase in the amount of audio data and especially music collections demand an efficient method to automatically retrieve audio objects based on its content. In this paper, based on the Gabor wavelet features, we will propose a method for content-based retrieval of perceptually similar music pieces in audio documents. It allows the user to select a reference passage within an audio file and retrieve perceptually similar passages such as repeating phrases within a music piece, similar music clips in a database or one song sung by different persons or in different languages. The proposed method will first divide an audio stream into clips, each of which contains one-second audio information. Then, the frame-based features of each clip are extracted based on the Gabor wavelet filters. Finally, a similarity measuring technique is provided to perform pattern matching on the resulting sequences of feature vectors. Experimental results show that the proposed method can achieve over 96% accuracy rate for audio retrieval.

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Yoo, Hun-Woo, Dong-Sik Jang, Seh-Hwan Jung, Jin-Hyung Park, and Kwang-Seop Song. "Visual information retrieval system via content-based approach." Pattern Recognition 35, no. 3 (March 2002): 749–69. http://dx.doi.org/10.1016/s0031-3203(01)00072-3.

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Kunt, M. "Special Issue on: Content-based Visual Information Retrieval." Signal Processing 82, no. 2 (February 2002): 327. http://dx.doi.org/10.1016/s0165-1684(01)00200-6.

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Kunt, M. "Special Issue on: Content-based Visual Information Retrieval." Signal Processing 82, no. 5 (May 2002): 819. http://dx.doi.org/10.1016/s0165-1684(02)00229-3.

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Simeoni, Fabio, Murat Yakici, Steve Neely, and Fabio Crestani. "Metadata harvesting for content-based distributed information retrieval." Journal of the American Society for Information Science and Technology 59, no. 1 (2007): 12–24. http://dx.doi.org/10.1002/asi.20694.

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19

Shih-Fu Chang. "Content-based indexing and retrieval of visual information." IEEE Signal Processing Magazine 14, no. 4 (July 1997): 45–48. http://dx.doi.org/10.1109/79.598595.

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Geradts, Zeno, and Jurrien Bijhold. "Content Based Information Retrieval in Forensic Image Databases." Journal of Forensic Sciences 47, no. 2 (March 1, 2002): 15245J. http://dx.doi.org/10.1520/jfs15245j.

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KANG, B., D. KIM, and S. LEE. "Exploiting concept clusters for content-based information retrieval." Information Sciences 170, no. 2-4 (February 25, 2005): 443–62. http://dx.doi.org/10.1016/j.ins.2004.03.013.

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Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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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 and Information Retrieval approach which can help users to acquire more accurate contents from the web. The Query Expansion component finds expanded candidate words of the query word through WordNet which contains synonyms in several different senses; In the Information Retrieval component, the query word and its candidate words are used together as the input of the search module to get the result items. Furthermore, we can put the result items into different classes based on the expansion. Some experiments and the results are described in the late part of this paper.

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Djeraba, C. "Association and content-based retrieval." IEEE Transactions on Knowledge and Data Engineering 15, no. 1 (January 2003): 118–35. http://dx.doi.org/10.1109/tkde.2003.1161586.

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Ali, Jafar M. "Content-Based Image Classification and Retrieval." International Journal of Intelligent Information Technologies 3, no. 3 (July 2007): 41–58. http://dx.doi.org/10.4018/jiit.2007070103.

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Güld, M. O., C. Thies, B. Fischer, K. Spitzer, D. Keysers, H. Ney, M. Kohnen, H. Schubert, B. B. Wein, and T. M. Lehmann. "Content-based Image Retrieval in Medical Applications." Methods of Information in Medicine 43, no. 04 (2004): 354–61. http://dx.doi.org/10.1055/s-0038-1633877.

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Summary Objectives: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Methods: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Results: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

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Mu, Xiangming. "Semantic visual features in content-based video retrieval." Proceedings of the American Society for Information Science and Technology 43, no. 1 (October 10, 2007): 1–14. http://dx.doi.org/10.1002/meet.1450430153.

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Hung, Chia-Ching. "A study on a content-based image retrieval technique for Chinese paintings." Electronic Library 36, no. 1 (February 5, 2018): 172–88. http://dx.doi.org/10.1108/el-10-2016-0219.

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Purpose The purpose of this study is to build a database of digital Chinese painting images and use the proposed technique to extract image and texture information, and search images similar to the query image based on colour histogram and texture features in the database. Thus, retrieving images by this image technique is expected to make the retrieval of Chinese painting images more precise and convenient for users. Design/methodology/approach In this study, a technique is proposed that considers spatial information of colours in addition to texture feature in image retrieval. This technique can be applied to retrieval of Chinese painting images. A database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. The authors develop an image-retrieval technique that considers colour distribution, spatial information of colours and texture. Findings In this study, a database of 1,200 digital Chinese painting images in three categories was built, including landscape, flower and figure. An image-retrieval technique was developed that considers colour distribution, spatial information of colours and texture. Through adjustment of feature values, this technique is able to process both landscape and portrait images. This technique also addresses liubai (i.e. blank) and text problems in the images. The experimental results confirm high precision rate of the proposed retrieval technique. Originality/value In this paper, a novel Chinese painting image-retrieval technique is proposed. Existing image-retrieval techniques and the features of Chinese painting are used to retrieve Chinese painting images. The proposed technique can exclude less important image information in Chinese painting images for instance liubai and calligraphy while calculating the feature values in them. The experimental results confirm that the proposed technique delivers a retrieval precision rate as high as 92 per cent and does not require a considerable computing power for feature extraction. This technique can be applied to Web page image retrieval or to other mobile applications.

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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.

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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.

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Wei, Xian Min. "Grid-Based Web Information Retrieval Architecture Professional Content-Oriented." Applied Mechanics and Materials 39 (November 2010): 103–6. http://dx.doi.org/10.4028/www.scientific.net/amm.39.103.

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By the use of grid computing, cluster systems, XML and other technologies, distributed Web information resources were organized and managed effectively by professional content and region from the logic, to design grid-based Web information retrieval architecture for professional content, to provide one way.for the user efficient access to information resources for professional content.

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Zhou, Guang-Tong, Kai Ming Ting, Fei Tony Liu, and Yilong Yin. "Relevance feature mapping for content-based multimedia information retrieval." Pattern Recognition 45, no. 4 (April 2012): 1707–20. http://dx.doi.org/10.1016/j.patcog.2011.09.016.

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Yun, Bo-Hyun, and Chang-Ho Seo. "Semantic-Based Information Retrieval for Content Management and Security." Computational Intelligence 19, no. 2 (May 2003): 87–110. http://dx.doi.org/10.1111/1467-8640.00215.

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Cha Zhang and Tsuhan Chen. "An active learning framework for content-based information retrieval." IEEE Transactions on Multimedia 4, no. 2 (June 2002): 260–68. http://dx.doi.org/10.1109/tmm.2002.1017738.

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Zachary, John, and S. S. Iyengar. "Information theoretic similarity measures for content based image retrieval." Journal of the American Society for Information Science and Technology 52, no. 10 (2001): 856–67. http://dx.doi.org/10.1002/asi.1139.

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Cao, WenMing, Ning Liu, QiCong Kong, and Hao Feng. "Content-based image retrieval using high-dimensional information geometry." Science China Information Sciences 57, no. 7 (April 23, 2014): 1–11. http://dx.doi.org/10.1007/s11432-014-5086-8.

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Gondra, Iker, and Douglas R. Heisterkamp. "Content-based image retrieval with the normalized information distance." Computer Vision and Image Understanding 111, no. 2 (August 2008): 219–28. http://dx.doi.org/10.1016/j.cviu.2007.11.001.

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Ahmed, Khawaja Tehseen, Shahida Ummesafi, and Amjad Iqbal. "Content based image retrieval using image features information fusion." Information Fusion 51 (November 2019): 76–99. http://dx.doi.org/10.1016/j.inffus.2018.11.004.

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Kokare, Manesh, and Nilesh Bhosle. "Random forest-based active learning for content-based image retrieval." International Journal of Intelligent Information and Database Systems 13, no. 1 (2020): 72. http://dx.doi.org/10.1504/ijiids.2020.10030218.

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Bhosle, Nilesh, and Manesh Kokare. "Random forest-based active learning for content-based image retrieval." International Journal of Intelligent Information and Database Systems 13, no. 1 (2020): 72. http://dx.doi.org/10.1504/ijiids.2020.108223.

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Mittal, Ankush, and Sumit Gupta. "Automatic content-based retrieval and semantic classification of video content." International Journal on Digital Libraries 6, no. 1 (February 2006): 30–38. http://dx.doi.org/10.1007/s00799-005-0119-y.

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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 (January 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 appear at the top of the results, SemFlickr takes the ontological relations among the given query terms to assign tag scores and then generates its ranked results. Experimental outcomes are encouraging and reveal a number of useful insights for developing applications that integrate the Semantic Web and Web 2.0 together.

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Tarjoman, Mana, Emad Fatemizadeh, and Kambiz Badie. "A Content-Based Approach to Medical Images Retrieval." International Journal of Healthcare Information Systems and Informatics 8, no. 2 (April 2013): 15–27. http://dx.doi.org/10.4018/jhisi.2013040102.

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Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

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Gururaj, C., and Satish Tunga. "AI Based Feature Extraction Through Content Based Image Retrieval." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4050–54. http://dx.doi.org/10.1166/jctn.2020.9018.

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Therapeutic pictures are progressively being utilized inside human services for conclusion, arranging treatment, controlling treatment and checking sickness movement. In reality, helpful imaging prevalently shapes vague, missing, dubious, essential, clashing, dull restricting, contorted data additionally, information has a strong fundamental character. The proposed approach can be used to achieve the accuracy by using the artificial intelligence techniques wherein the disease level is identified by comparing it with the artificial intelligence data. The two fold merit of this system is it provides better accuracy and also determines all the possibilities of spreading of the disease including the various stages of the disease. This research work also represents new automated strategies of the division and arrangement of therapeutic pictures utilizing computerized reasoning, i.e., delicate processing strategies, data combination and particular area information. Promising outcomes demonstrate the predominance of the delicate processing and information based approach over best customary systems as far as division mistakes. The arrangement of various structures is made by executing rules obtained by both space literature and by medical experts.

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Wood, Norman B., and Tristan S. L'Ecuyer. "What millimeter-wavelength radar reflectivity reveals about snowfall: an information-centric analysis." Atmospheric Measurement Techniques 14, no. 2 (February 4, 2021): 869–88. http://dx.doi.org/10.5194/amt-14-869-2021.

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Abstract. The ability of single-frequency, millimeter-wavelength radar reflectivity observations to provide useful constraints for retrieval of snow particle size distribution (PSD) parameters, snowfall rates, and snowfall accumulations is examined. An optimal estimation snowfall retrieval that allows analyses of retrieval uncertainties and information content is applied to observations of near-surface W-band reflectivities from multiple snowfall events during the 2006–2007 winter season in southern Ontario. Retrieved instantaneous snowfall rates generally have uncertainties greater than 100 %, but single-event and seasonal snow accumulations from the retrieval results match well with collocated measurements of accumulations. Absolute fractional differences are mainly below 30 % for individual events that have more substantial accumulations and, for the season, 12.6 %. Uncertainties in retrieved snowfall rates are driven mainly by uncertainties in the retrieved PSD parameters, followed by uncertainties in particle model parameters and, to a lesser extent, the uncertainties in the fall-speed model. Uncertainties attributable to assuming an exponential distribution are negligible. The results indicate that improvements to PSD and particle model a priori constraints provide the most impactful path forward for reducing uncertainties in retrieved snowfall rates. Information content analyses reveal that PSD slope is well-constrained by the retrieval. Given the sensitivity of PSD slope to microphysical transformations, the results show that such retrievals, when applied to radar reflectivity profiles, could provide information about microphysical transformations in the snowing column. The PSD intercept is less well-constrained by the retrieval. While applied to near-surface radar observations in this study, the retrieval is applicable as well to radar observations aloft, such as those provided by profiling ground-based, airborne, and satellite-borne radars under lighter snowfall conditions when attenuation and multiple scattering can be neglected.

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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 (June 30, 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 data and inaccuracy, which often result in the showing of irrelevant images or videos. The authors of the proposed study integrate image and visual data to improve the precision of the retrieved results for both photographs and videos. In response to a user's query, this study investigates alternative ways for fetching high-quality photos and related videos.

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Bashir, MohammedBakri, MuhammadShafie Abd Latiff, AboamamaAtahar Ahmed, Adil Yousif, and ManhalElfadil Eltayeeb. "Content-based Information Retrieval Techniques Based on Grid Computing: A Review." IETE Technical Review 30, no. 3 (2013): 223. http://dx.doi.org/10.4103/0256-4602.113511.

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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 panels. We make a quantitative estimate by projecting from data from the Cross-Language Evaluation Forum (Image-CLEF) campaigns, and qualitatively validate it through experiments using the Image Retrieval in Medical Applications (IRMA) project. Results: Based on 2077 articles with 11,753 pages, 4493 figures, and 11,238 individual images, the predicted accuracy for article retrieval may reach 97.08%. Conclusions: Therefore, CBIR potentially has a high impact in medical literature search and retrieval.

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Narasimhalu, A. Desai. "Special section on content-based retrieval." Multimedia Systems 3, no. 1 (February 1995): 1–2. http://dx.doi.org/10.1007/bf01236574.

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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 (June 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 the user. Through a training process using the AdaBoost algorithm9 our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.

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Schnorrenberg, F., C. S. Pattichis, C. N. Schizas, and K. Kyriacou. "Content-based retrieval of breast cancer biopsy slides." Technology and Health Care 8, no. 5 (November 1, 2000): 291–97. http://dx.doi.org/10.3233/thc-2000-8505.

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