• Journal of Internet Computing and Services
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• v.1 no.2
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• pp.89-96
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• 2000

Relevance feedback is a technique to learn the user's subjective perception of similarity between images, and has recently gained attention in Content Based Image Retrieval. Most relevance feedback methods assume that the individual features that are used in similarity judgments do not interact with each other. However, this assumption severely limits the types of similarity judgments that can be modeled In this paper, we explore a more sophisticated model for similarity judgments based on fuzzy measures and the Choquet Integral, and propose a suitable algorithm for relevance feedback, Experimental results show that the proposed method is preferable to traditional weighted- average techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• 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 Korean Society for Library and Information Science
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• v.50 no.1
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• pp.417-435
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• 2016

Relevance judgment is important in terms of improving the effectiveness of information retrieval systems, and it has been dominant for users to search and use images utilizing internet and digital technologies. However, in the field of image retrieval, there have been only a few studies in terms of identifying relevance criteria. The purpose of this study aims to identify and characterize the non-relevance criteria from the failed image searches. In order to achieve the purpose of this study, a total of 135 participants were recruited and a total of 1,452 criteria items were collected for this study. Analyses and identification on the data set found thirteen criteria such as 'topicality', 'visual content', 'accuracy', 'visual feature', 'completeness', 'appeal to user', 'focal point', 'bibliographic information', 'impression', 'posture', 'face feature', 'novelty', and 'time frame'. Among these criteria, 'visual content' and 'focal point' were introduced in this current study, while 'action' criterion identified in previous studies was not shown in this current study. When image needs and image uses are analyzed with these criteria, there are distinctive differences depending on different image needs and uses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.249-268
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• 2014

Relevance feedback is an effective tool to bridge the gap between superficial image contents and medically-relevant sense in content-based medical image retrieval. In this paper, we propose an interactive medical image search framework based on pairwise constraint propagation. The basic idea is to obtain pairwise constraints from user feedback and propagate them to the entire image set to reconstruct the similarity matrix, and then rank medical images on this new manifold. In contrast to most of the algorithms that only concern manifold structure, the proposed method integrates pairwise constraint information in a feedback procedure and resolves the small sample size and the asymmetrical training typically in relevance feedback. We also introduce a long-term feedback strategy for our retrieval tasks. Experiments on two medical image datasets indicate the proposed approach can significantly improve the performance of medical image retrieval. The experiments also indicate that the proposed approach outperforms previous relevance feedback models.

• Journal of the Korean School Mathematics Society
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• v.8 no.2
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• pp.223-248
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• 2005

This study is to survey and analyze the opinion of teachers about the relevance of educational content in the 7th mathematics curriculum. For the purpose of this study, we analyze the result of the questionnaire survey which consists in the question about the relevance(Quantity, level, validity) of educational content in the 7th mathematics curriculum. 515 elementary school teachers, 314 middle school teachers, and 323 high school teachers are participated in this survey. 75 percent of elementary school teachers think that the educational quantity must be reduced for the relevance of educational content. So do 50 percent of secondary school teachers. Both of them think that the number of topic must be reduced for the relevance. In special, this study shows that the response rate about the object which is related with interest is very low compared with any other mathematics education objects. So, it is necessary to pay more attention to the object which is related with interest.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.746-752
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• 2003

In this paper, we proposed an image retrieval system with a simple and effective relevance feedback, called RAP(Reward and Punishment) algorithm. First, color and texture features were extracted from the images. Next, the extracted feature values were used for image retrieval in various forms. We applied the relevance feedback to the initial retrieved images from the image retrieval system, and compared its result with that of the conventional system. In the experiment using the test image database of 16 class 512 images, the proposed system showed the better retrieval performance of about 10∼l7 % than that of the conventional INRIA system in each relevance feedback step.

• ETRI Journal
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• v.33 no.2
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• pp.240-250
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• 2011

To enable a relevance feedback paradigm to evolve itself by users' feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.119-132
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• 2021

Identification of associated knowledge based on content relevance is a fundamental functionality in managing service and security of core knowledge. This study compares the performance of methods to identify associated knowledge based on content relevance, i.e., the associated document network composition performance of keyword-based and word-embedding approach, to examine which method exhibits superior performance in terms of quantitative and qualitative perspectives. As a result, the keyword-based approach showed superior performance in core document identification and semantic information representation, while the word embedding approach showed superior performance in F1-Score and Accuracy, association intensity representation, and large-volume document processing. This study can be utilized for more realistic associated knowledge service management, reflecting the needs of companies and users.

• Journal of Information Science Theory and Practice
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• v.1 no.2
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• pp.16-35
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• 2013

Relevance has a long history of scholarly investigation and discussion in information science. One of its notable concepts is that of 'user-based' relevance. The purpose of this study is to examine how users construct their perspective on the concept of relevance; to analyze what the constituent elements (facets) of relevance are, in terms of core-periphery status; and to compare the difference of constructions of two groups of users (information users vs. information professionals) as applied with a social representations theory perspective. Data were collected from 244 information users and 123 information professionals through use of a free word association method. Three methods were employed to analyze data: (1) content analysis was used to elicit 26 categories (facets) of the concept of relevance; (2) structural analysis of social representations was used to determine the core-periphery status of those facets in terms of coreness, sum of similarity, and weighted frequency; and, (3) maximum tree analysis was used to present and compare the differences between the two groups. Elicited categories in this study overlap with the ones from previous relevance studies, while the findings of a core-periphery analysis show that Topicality, User-needs, Reliability/Credibility, and Importance are configured as core concepts for the information user group, while Topicality, User-needs, Reliability/Credibility, and Currency are core concepts for the information professional group. Differences between the social representations of relevance revealed that Topicality was similar to User-needs and to Importance. Author is closely related to Title while Reliability/Credibility is linked with Currency. Easiness/Clarity is similar to Accuracy. Overall, information users and professionals function with a similar social collective of shared meanings for the concept of relevance. The overall findings identify the core and periphery concepts of relevance and their relationships in terms of coreness, similarity, and weighted frequency.