• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.261-264
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• 2000

A database was constructed for customers to find and choose and apparel product. The product information was collected from words on several kinds of advertisement, which purchasers meet in their ordinary life. The information was sorted and categorized in 8 basic mother categories (Target group, Color, Apparel type, Fabric, Size, Price, Manufacturer) to build a suitable database structure. Each mother category was arranged into subcategories. Also this study showed a demonstration version of the retrieval system. the research resulted can be used for more intensive retrieval system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.3-8
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• 2002

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter- and intra- relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the algorithms for content-based retrieval for multimedia database systems.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.197-206
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• 2010

For an emotion retrieval system implementation to support pedestrian navigation, coordinating the pedestrian emotion model with the system user's emotion is considered a key component. This study proposes a new method for capturing the user's model that corresponds to the pedestrian emotion model and examines the validity of the method. In the first phase, a database comprising a set of interior images that represent hypothetical destinations was developed. In the second phase, 10 subjects were recruited and asked to evaluate on navigation and satisfaction toward each interior image in five rounds of navigation experiments. In the last phase, the subjects' feedback data was used for of the pedestrian emotion model, which is called ‘learning' in this study. After evaluations by the subjects, the learning effect was analyzed by the following aspects: recall ratio, precision ratio, retrieval ranking, and satisfaction. Findings of the analysis verify that all four aspects significantly were improved after the learning. This study demonstrates the effectiveness of the learning algorithm for the proposed pedestrian emotion model. Furthermore, this study demonstrates the potential of such pedestrian emotion model to be well applicable in the development of various mobile contents service systems dealing with visual images such as commercial interiors in the future.

• Science of Emotion and Sensibility
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• v.7 no.1
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• pp.51-64
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• 2004

In this paper, we propose a web page retrieval system based on the sensibility evaluation induced by the color combination of web pages. The realized system consist of two modules - the indexing module that automatically extracts and indexes the color information from the web page and the retrieval module that retrieves web pages based on the color combination when sensibility adjective is presented. Also, to verify the system usefulness, we analyzed the ranking of web pages retrieved by the system and by human subjects (non-expels and experts for color web page design) using two statistical methods of correlation and paired-t test. Results by non-experts showed the realized system was suitable for 10 sensibility adjectives among 18 sensibility adjectives, and results by experts showed that the realized system was suitable for 14 sensibility adjectives among 18 sensibility adjectives.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.501-510
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• 2010

The knowledge base on emotional information is one of the key elements in the implementation of emotion retrieval systems for contents design of Mobile devices. This study proposed a new approach to the knowledge base implementation by automatically extracting color components from full-color images. In this study, the validity of the proposed method was empirically tested. Database was developed using 100 interior images as visual stimuli and a total of 48 subjects participated in the experiment. In order to test the reliability of the proposed 'emotional information knowledge base', firstly 'recall ratio' that refers to frequencies of correct images from the retrieved images was derived. Secondly, correlation Analysis was performed to compare the ratings by the subjects to what the system calculated. Finally, the rating comparison was used to run a paired-sample t-test. The analysis demonstrated satisfactory recall ration of 62.1%. Also, a significant positive correlation (p<.01) was observed from all the emotion keywords. The paired Sample t-test found that all the emotion keywords except "casual" retrieved the images in the order from more relevant to less relevant images and the difference was statistically significant (t(9)=5.528, p<.05). Findings of this study support that the proposed 'emotional information knowledge base' established only with color information automatically extracted from images can be effectively used for such visual stimuli search tasks as commercial interior images.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.124-128
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• 2011

Music emotion recognition is currently one of the most attractive research areas in music information retrieval. In order to use emotion as clues when searching for a particular music, several music based emotion recognizing systems are fundamentally utilized. In order to maximize user satisfaction, the recognition accuracy is very important. In this paper, we develop a new music emotion recognition system, which employs a multilabel feature selector and multilabel classifier. The performance of the proposed system is demonstrated using novel musical emotion data.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.43-52
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• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.83-88
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• 2013

Many studies adopting brain functions to the engineering systems have been made for recent years as the brain Science has developed. If we investigate the parts which take part in memorizing and emotional process, we can know that Hippocampus of memorizing center and Amygdala of Emotional center closely cooperate each other. Actually Knowledge effects on Emotion and Emotion effects on Knowledge. During the human decision making, emotional factor has much important effects on Decision making process. For implementing more delicate intelligent system, the knowledge base coupled to emotional factor should be designed. Accordingly in this paper starting from the idea of cooperating system between Hippocampus and Amygdala,, we design Knowledge Emotion Binding System and propose Emotional changing mechanism by Memory retrieval and knowledge reasoning process.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.55-67
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• 2014

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

• Journal of Korea Game Society
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• v.17 no.3
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• pp.45-52
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• 2017

This paper proposes a new strategy of emotion-based music visualization. Emotional LED lighting control system is suggested to help audiences enhance the musical experience. In the system, emotion in music is recognized by a proposed algorithm using a dimensional approach. The algorithm used a method of music emotion variation detection to overcome some weaknesses of Thayer's model in detecting emotion in a one-second music segment. In addition, IRI color model is combined with Thayer's model to determine LED light colors corresponding to 36 different music emotions. They are represented on LED lighting control system through colors and animations. The accuracy of music emotion visualization achieved to over 60%.