• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.79-82
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• 2015

Bag of visual words(BoVW) is one of the image classification and retrieval methods, using feature point that automatical sorting and searching system by image feature vector of data base. The existing method using feature point shall search or classify the image that user unwanted. To solve this weakness, when comprise the words, include not only feature point but color information that express overall mood of image or texture information that express repeated pattern. It makes various searching possible. At the test, you could see the result compared between classified image using the words that have only feature point and another image that added color and texture information. New method leads to accuracy of 80~90%.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.323-328
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• 2011

For content-based image retrieval, the earth mover's distance and the optimal color composition distance are proposed to measure the dissimilarity. Although providing good retrieval results, both methods are too time-consuming to be used in a large image database. To solve the problem, we propose a new distance function that calculates an approximate earth mover's distance in linear time. To calculate the dissimilarity in linear time, the proposed approach employs the space-filling curve. We have performed extensive experiments to show the effectiveness and efficiency of the proposed approach. The results reveal that our approach achieves almost the same results with the EMD in linear time.

• New & Renewable Energy
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• v.9 no.3
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• pp.14-19
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• 2013

Comparative evaluation of offshore wind retreival software, which use the satellite images taken by Synthetic Aperture Radar sensor; SARTools of CLS-SOPRONO, France and SpaceEye of London Research and Development Corporation, Canada is carried out. For a reference satellite image, ENVISAT ASAR imagery of Jeollanam-do Wan-do area when the winter-time northwestern wind prevails is processed by CMOD_IFR2, CMOD4, CMOD5 algorithms. Wind speed difference and its relative ratio are calculated to evaluate uncertainty of software selection.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.1
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• pp.53-73
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• 2014

Given that emotion-based computing environment has grown recently, it is necessary to focus on emotional access and use of multimedia resources including images. The purpose of this study aims to identify the visual cues for emotion in images. In order to achieve it, this study selected five basic emotions such as love, happiness, sadness, fear, and anger and interviewed twenty participants to demonstrate the visual cues for emotions. A total of 620 visual cues mentioned by participants were collected from the interview results and coded according to five categories and 18 sub-categories for visual cues. Findings of this study showed that facial expressions, actions / behaviors, and syntactic features were found to be significant in terms of perceiving a specific emotion of the image. An individual emotion from visual cues demonstrated distinctive characteristics. The emotion of love showed a higher relation with visual cues such as actions and behaviors, and the happy emotion is substantially related to facial expressions. In addition, the sad emotion was found to be perceived primarily through actions and behaviors and the fear emotion is perceived considerably through facial expressions. The anger emotion is highly related to syntactic features such as lines, shapes, and sizes. Findings of this study implicated that emotional indexing could be effective when content-based features were considered in combination with concept-based features.