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Intelligent Medical Image SearchIntelligente Bildsuche in der Medizin

Sonja Zillner, Martin Huber
2009 it - Information Technology  
In this article we present the project MEDICO that strives to implement intelligent medical image search by means of machine learning algorithms and semantic technologies.  ...  an Bedeutung.  ...  Usually, CBIR systems are queried by example images and classifiers such as nearest neighbor are used to compare the feature vector of the query image with those stored in the database.  ... 
doi:10.1524/itit.2009.0553 fatcat:343lq4l55rcc3crmadpthfweyu

Semantic clusters based manifold ranking for image retrieval

Ran Chang, Xiaojun Qi
2011 2011 18th IEEE International Conference on Image Processing  
Extensive experiments demonstrate our system outperforms the other manifold systems and SVM-based systems in the context of both correct and erroneous feedback.  ...  Specifically, we apply the SVM-based relevance feedback technique to create semantic clusters for computing the reliability score of each database image.  ...  The remaining 90% of the database images (i.e., the images not used in the training process) are used as queries for all the experiments.  ... 
doi:10.1109/icip.2011.6116133 dblp:conf/icip/ChangQ11 fatcat:bfenevvchrcevhlanmxzhwocle

Learning a weighted semantic manifold for content-based image retrieval

Ran Chang, Zhongmiao Xiao, KokSheik Wong, Xiaojun Qi
2012 2012 19th IEEE International Conference on Image Processing  
Extensive experiments demonstrate our system outperforms other manifold systems and learning systems in the context of both correct and erroneous feedback.  ...  Specifically, we apply the learning mechanism to capture users' semantic concepts in clusters and extract high-level semantic features for each database image.  ...  To facilitate the evaluation process, we designed an automatic feedback scheme to construct the semantic manifold structure by performing query sessions using 10% unique, randomly chosen database images  ... 
doi:10.1109/icip.2012.6467381 dblp:conf/icip/ChangXWQ12 fatcat:fezckhwzk5csbjamefp7sopsei

Significance of Context Sensitiveness in Content based Image Retrieval System and Bridging the Semantic Gap

N. Karthikeyan, R. Dhanapal
2013 International Journal of Computer Applications  
in obtaining images according to their queries.  ...  In this paper, we discuss the principal challenges facing CBIR systems and ways in which they could be overcome.  ...  The query is an image and the user wishes to find images in the database that contain objects present in the query image.  ... 
doi:10.5120/14594-2833 fatcat:4vvn2etvyjc35bbmr5x3odcbz4

Textual Query Based Image Retrieval

Patil Patil
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
Real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions. Then user provides a textual query.  ...  Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users interests, has been an active and fast advancing research area  ...  Annotating images [3] manually is a hectic task and expensive for large image databases, and is often subjective, context-sensitive and incomplete.  ... 
doi:10.17762/ijritcc2321-8169.150322 fatcat:hqohttalvnb77kzqb6ps7lp32e

Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content [chapter]

Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo, Josep Lladós
2011 Lecture Notes in Computer Science  
In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries.  ...  Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues.  ...  In our system, all the trademarks in the database have an associated list of Vienna codes. We can see in Figure 2 a set of trademark images all belonging to the same category.  ... 
doi:10.1007/978-3-642-20161-5_32 fatcat:3dkm23mn6bhojai345jyrxgskm

The Use of Ontology in Retrieval: A Study on Textual, Multilingual and Multimedia Retrieval

Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Nasir Mahmood, Waqar Mahmood
2019 IEEE Access  
Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data.  ...  Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word.  ...  In the retrieval process of images, user feeds the system images as an example, these example images are converted into a feature vector and compared with those database feature vector [102] . while the  ... 
doi:10.1109/access.2019.2897849 fatcat:ei2zxyxdjndbvgzzue2indwqy4

A knowledge-based approach for retrieving images by content

Chih-Cheng Hsu, W.W. Chu, R.K. Taira
1996 IEEE Transactions on Knowledge and Data Engineering  
retrieval approach is scalable and context-sensitive.  ...  The performance of the proposed knowledge-based query processing is also discussed.  ...  Dionisio for implementation of the graphical user interface of the query language, Christine Chih for her assistance in image segmentation, Kuorong Chiang and Timothy Plattner for developing the programs  ... 
doi:10.1109/69.536245 fatcat:3rhlhpoe2vhupa7ld2bmtzv5ru

Ontology of Gaps in Content-Based Image Retrieval

Thomas M. Deserno, Sameer Antani, Rodney Long
2008 Journal of digital imaging  
In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability.  ...  The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing  ...  They are formulated in such a way that they can be used in a variety of semantic contexts of medicine, where CBIR systems are applied.  ... 
doi:10.1007/s10278-007-9092-x pmid:18239964 pmcid:PMC3043678 fatcat:gcqzg3mbhrhkbobmh3ttqq4mta

A Survey on Content-based Image Retrieval

Mohamed Maher
2017 International Journal of Advanced Computer Science and Applications  
The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases.  ...  The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields.  ...  ACKNOWLEDGMENT This work was supported by the Research Centre of the College of Computer and Information Sciences, King Saud University. The author is grateful for this support.  ... 
doi:10.14569/ijacsa.2017.080521 fatcat:kzfskamd25coxcj3537z6z3ty4

Using Word Embedding to Enable Semantic Queries in Relational Databases

Rajesh Bordawekar, Oded Shmueli
2017 Proceedings of the 1st Workshop on Data Management for End-to-End Machine Learning - DEEM'17  
WE enables novel capabilities such as the controlled disclosure of database information in a variety of ways.  ...  The vectors are used in the existing SQL query infrastructure via UDFs.  ...  For example, the goal of query shown in Figure 1 is to identify all images that are similar to every image in the set of user chosen images.  ... 
doi:10.1145/3076246.3076251 dblp:conf/sigmod/BordawekarS17 fatcat:o2ljou52kzf4naminuaqduatga

Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities [article]

Rajesh Bordawekar and Bortik Bandyopadhyay and Oded Shmueli
2017 arXiv   pre-print
We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images.  ...  We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.  ...  of using semantic similarities in the context of a traditional SQL aggregation query.  ... 
arXiv:1712.07199v1 fatcat:ltwgviux6rhmplja5xeqekxoj4

An Ontology Based Framework for Retrieval of Museum Artifacts

Manoj Kumar Sharma, Tanveer J. Siddiqui
2016 Procedia Computer Science  
The dataset consists of images displayed in various galleries of Allahabad museum along with their textual description.  ...  It supports semantic retrieval by combining ontological concepts, visual and textual features automatically extracted from images and their textual descriptions.  ...  Acknowledgements We thank Director, Allahabad Museum for permitting us to collect images to create a dataset.  ... 
doi:10.1016/j.procs.2016.04.083 fatcat:yyjtxc5u5jezxip6qsdvifpaf4

Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval

Karm VeerSingh, Anil K. Tripathi
2012 International Journal of Computer Applications  
We propose Contextual Query Perfection Scheme (CQPS) to learn, refine the current context that could be used in query perfection in RF cycle to understand the semantic of query on the basis of relevance  ...  Hence we are exploiting the plausibility of context associated with semantic concept in retrieving relevance information.  ...  2) How system will identify current context in a query?  ... 
doi:10.5120/8834-3052 fatcat:hvvl23mcqjfbhf2z6iecpq26pm

Knowledge Assisted Analysis and Categorization for Semantic Video Retrieval [chapter]

Manolis Wallace, Thanos Athanasiadis, Yannis Avrithis
2004 Lecture Notes in Computer Science  
In this paper we discuss the use of knowledge for the analysis and semantic retrieval of video.  ...  During retrieval, the context of the query is used to clarify the exact meaning of the query terms and to meaningfully guide the process of query expansion and index matching.  ...  The Notion of Context In the processes of video content and user query analysis we utilize the common meaning of semantic entities.  ... 
doi:10.1007/978-3-540-27814-6_65 fatcat:cyqtnkjt3rfonjluxu7vgclv2i
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