• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.867-875
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• 2009

More and richer information sources and services are available on the web everyday. However, harmful information, such as adult content, is not appropriate for all users, notably children. Since internet is a worldwide open network, it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse ways, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies and examine which algorithms for feature selection, the process that select content terms, as features, can be useful for text categorization in all content term occurs in documents, are suitable for classifying harmful contents through implementation and experiment.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.131-138
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• 2009

This paper is about adult certification on Mobile RFID Service Environments coming through the combination of RFID(Radio Frequency IDentification) technology, as the core technology of ubiquitous environments, and mobile telecommunication technology. To overcome the shortcoming of simple adult certification on current internet, we suggest a framework for content-based classification and propose an adult certification system using it. At first, we explain conventional methods for adult certification, and show a criteria of content-based classification for preventing an exposure of adult contents from minors. Additionally, we describe data structure and system for the proposed adult certification on mobile RFID environment, and finish it with concluding remarks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.63-66
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• 1996

In this paper, we propose a new content-based color image retrieval algorithm. The algorithm makes use of two features; colors as global features and block classification results as local features. More specifically, we obtain R, G, B color histograms and classify nonoverlapping small image blocks into texture, monotone, and various edges, then using these histograms and classification results were make a similarity measure. Experimental results show that retrieval rate of the proposed algorithm is higher than the previous method.

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.

• ETRI Journal
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• v.42 no.1
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• pp.67-77
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• 2020

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.545-557
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• 2014

Purpose: The purpose of this study was to develop a patient classification system based on nursing care intensity for patients with acute stroke-related symptoms and verify its validity and reliability. Methods: Data were collected between November, 2013 and February, 2014. The verification for content validity of the patient classification system was conducted by a group of seven professionals. Both interrater reliability and concurrent validity were verified at stroke units in tertiary hospitals. Results: The intensive nursing care for acute stroke patients consisted of 14 classified domains and 56 classified contents by adding 'neurological assessment and observation' and 'respiratory care': 'hygiene', 'nutrition', 'elimination', 'mobility and exercise', 'education or counselling', 'emotional support', 'communication', 'treatment and examination', 'medication', 'assessment and observation', 'neurological assessment and observation', 'respiratory care', 'coordination between departments', and 'discharge or transfer care'. Each domain was classified into four levels such as Class I, Class II, Class III, and Class IV. Conclusion: The results show that this patient classification system has satisfactory validity for content and concurrent and verified reliability and can be used to accurately estimate the demand for nursing care for patients in stroke units.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.25-34
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• 2017

With the recent spread of smartphones and the introduction of web services, online users can access large-scale content regardless of time or place. However, users have had trouble finding the content they wanted among large-scale content. To solve this problem, user modeling and content recommendation system have been actively studied in various fields. However, in spite of active changes in senior groups according to the changes in information environment, research on user modeling and content recommendation system focused on senior groups are insufficient. In this paper, we propose a method of modeling smart senior based on their preference, and further develop a smart senior classification model using machine learning methods. As a result, we can not only grasp the preferences of smart seniors, but also develop a smart senior classification model, which is the foundation for the research of a recommendation system which will provide the activities and contents most suitable for senior groups.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.182-189
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• 2002

In this paper, we research the content-based analysis and classification according to the composition of the feature parameters pool for the auditory signals to implement the auditory indexing and searching system. Auditory data is classified to the primitive various auditory types. we described the analysis and feature extraction method for the feature parameters available to the auditory data classification. And we compose the feature parameters pool in the indexing group unit, then compare and analysis the auditory data centering around the including level and indexing criterion into the audio categories. Based on this result, we composed the classification procedure and simulate the auditory data classification.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.