• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.3
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• pp.103-109
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• 2011

With the growing popularity of digital video applications, those areas of the efficient transmit, storage management, and search technology for video data are emerging as an important core technology. To be an effective video indexing system, users need to be able to find the video segments that they want. Unfortunately, video data is difficult to manage because of its unstructured data type and large volume with linear forms. This paper proposes a shot verification using visual rhythm and video retrieval system. The shot verification is designed to detect a segment from video easily and quickly, known as shot boundaries, just by changing the visual rhythm without playing the image. Therefore, this can decrease the false detected shots and generate the unidentified shots and key frames. The retrieval system is constructed in terms of visual descriptor from the list of MPEG-7. The effectiveness of the proposed shot verification process and video retrieval system is demonstrated.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.19-22
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• 2006

In this paper we present a vision based place recognition method which uses Bayesian method with feed back of image retrieval. Both Bayesian method and image retrieval method are based on interest features that are invariant to many image transformations. The interest features are detected using Harris-Laplacian detector and then descriptors are generated from the image patches centered at the features' position in the same manner of SIFT. The Bayesian method contains two stages: learning and recognition. The image retrieval result is fed back to the Bayesian recognition to achieve robust and confidence. The experimental results show the effectiveness of our method.

• Journal of the Korean Society for information Management
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• v.16 no.3
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• pp.49-63
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• 1999

The purpose of this study is to investigate whether the terms in queries are distributed according to the 2-Poisson model in the documents represented by abstract/title or full-text. In this study, retrieval experiments using Binary independence and 2-Poisson independence model, which are based on the probabilistic theory, were conducted to see if the 2-Poisson distribution of the query terms has an influence on the retrieval effectiveness, particularly of full-text information retrieval system.

• ETRI Journal
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• v.43 no.5
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• pp.856-868
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• 2021

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.41-56
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• 2005

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance. we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance.

• Journal of the Korean Society for information Management
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• v.12 no.2
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• pp.213-223
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• 1995

It has known that different representations of either queries or documents, or different retrieval techniques retrieve different sets of documents. Recent works suggest that significant improvements in retrieval performance can be achieved by combining multiple representations or multiple retrieval techniques. In this paper we propose a simple method for retrieving different documents within a single query representation, a single document representation and a single retrieval technique. We classify the types of documents, and describe the properties of weighting schemes. Then. we explain that different properties of weighting schemes may retrieve different types of documents. Experimental results show that significant improvements can be obtained by combining the retrieval results form different properties of weighting schemes.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2931-2946
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• 1997

In multimedia information retrieval applications, content-based image retrieval is essential for retrieving relevant multimedia documents. The purpose of our paper is to provide effective representation and efficient retrieval of images when a pixel-level original image is automatically or manually transformaed into its iconic image containing meaningful graphic descriptions, called icon objects. For this, we first propose new spatial match representationschemes to describe spatial relationships between icon objects accurately by expressing them as rectangles, rather than as points. In order to accelerate image searching, we also design an efficient retrieval method using a two-dimensional signature file organization. Finally, we show from our experiment that the proposed representation schemes achieve better retrieval effectiveness than the 9-DLT (Direction Lower Triangular) scheme.

• Journal of the Korean Society for Library and Information Science
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• v.21
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• pp.249-290
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• 1991

Theoretical developments in the information retrieval have offered a number of alternatives to traditional Boolean retrieval. Probability theory and fuzzy set theory have played prominent roles here. Fuzzy set theory is an attempt to generalize traditional set theory by permitting partial membership in a set and this means recognizing different degrees to which a document can match a request. In this study, an experimentation of a document retrieval system using the fuzzy relation matrix of the keywords is described and the results are offered. The queries composed of keywords and Boolean operaters AND, OR, NOT were processed in the retrieval method, and the method was implemented on the PC of 32bit level (30 MHz) in an experimental system. The measurement of the recall ratio and precision ratio verified the effectiveness of the proposed fuzzy relation matrix of keywords and retrieval method. Compared to traditional crisp method in the same document database, the recall ratio increased $10\%$ high although the precision ratio decreased slightly. The problems, in this experiment, to be resolved are first, the design of the automatic data input and fuzzy indexing modules, through which the system . can have the ability of competition and usefulness. Second, devising a systematic procedure for assigning fuzzy weights to keywords in documents and in queries.