• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.533-540
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• 2012

In this paper, we propose a document summarization method using the pseudo relevance feedback and the term weighting based on semantic features. The proposed method can minimize the user intervention to use the pseudo relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature. In addition, it uses the semantic feature of term weighting and the expanded query to reduce the semantic gap between the user's requirement and the result of proposed method. The experimental results demonstrate that the proposed method achieves better performant than other methods without term weighting.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.434-445
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• 2009

Content-based image retrieval(CBIR) is the retrieval technique which uses the contents of images. However, in contrast to text data, multimedia data are ambiguous and there is a big difference between system's low-level representation and human's high-level concept. So it doesn't always mean that near points in the vector space are similar to user. We call this the semantic-gap problem. Due to this problem, performance of image retrieval is not good. To solve this problem, the relevance feedback(RF) which uses user's feedback information is used. But existing RF doesn't consider user's region-of-interest(ROI), and therefore, irrelevant regions are used in computing new query points. Because the system doesn't know user's ROI, RF is proceeded in the image-level. We propose a new ROI RF method which guides a user to select ROI from relevant images for the retrieval of complex satellite image, and this improves the accuracy of the image retrieval by computing more accurate query points in this paper. Also we propose a pruning technique which improves the accuracy of the image retrieval by using images not selected by the user in this paper. Experiments show the efficiency of the proposed ROI RF and the pruning technique.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.193-205
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• 2007

Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1517-1524
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• 2011

In this paper, a new document summarization method, which uses the semantic features and the pseudo relevance feedback (PRF) by using WordNet, is introduced to extract meaningful sentences relevant to a user query. The proposed method can improve the quality of document summaries because the inherent semantic of the documents are well reflected by the semantic feature from NMF. In addition, it uses the PRF by the semantic features and WordNet to reduce the semantic gap between the high level user's requirement and the low level vector representation. The experimental results demonstrate that the proposed method achieves better performance that the other methods.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.99-106
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• 2010

In this paper, we propose the construction of multi-queries and consistency principle for the user's emotion-based music retrieval system. The features used in the system are MPEG-7 audio descriptors, which are international standards recommended for content-based audio retrievals. In addition we propose the method to determine the weight that represent the importance of each descriptor for each emotion in order to reduce the computation. Also, the proposed retrieval algorithm that uses the relevance feedback based on consistency principal and multi-queries improves the success ratio of musics corresponding to user's emotion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.387-388
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• 2012

In order to solve problem which User sometime visits the wrong page with respect to user intention when uses snippet. this paper proposes a new snippet extraction method using fuzzy. The proposed method uses pseudo relevance feedback to expand the use's query. It uses the fuzzy association between the expanded query and the web pages to extract snippet to be well reflected semantic user's intention.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1302-1306
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• 2004

This paper proposes a model for information filtering (IF) on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents by fuzzy, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. We modified user profile by the user's relevance feedback and discerning words in the documents. In experimental we compared the results of three methods, firstly is to search documents that are not passed the filtering system. Second, search documents that passed the filtering system. Lastly, search documents after modified user profile. The result from using these techniques can obtain higher precision.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.2
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• pp.259-276
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• 1998

This study is to contribute to develop adaptive information systems meeting inside information needs as well as represented information needs, and dealing with every levels of users and user's preferences. Also, this study is to present a method of developing adaptive information system through developing user profile using machine teaming and decision tree, applying relevance feedback using merged vector, and applying user feedback.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.53-60
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• 2012

In this paper, we proposes a new document summarization method using the expanded query by wikipedia and the semantic feature representing inherent structure of document set. The proposed method can expand the query from user's initial query using the relevance feedback based on wikipedia in order to reflect the user require. It can well represent the inherent structure of documents using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can reduce the semantic gap between the user require and the result of document summarization to extract the meaningful sentences using the expanded query and semantic features. The experimental results demonstrate that the proposed method achieves better performance than the other methods to summary document.