• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.398-400
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• 2019

Faced with the increasing demand for image recognition on mobile devices, how to run convolutional neural network (CNN) models on mobile devices with limited computing power and limited storage resources encourages people to study efficient model design. In recent years, many effective architectures have been proposed, such as mobilenet_v1, mobilenet_v2 and PeleeNet. However, in the process of feature selection, all these models neglect some information of shallow features, which reduces the capture of shallow feature location and semantics. In this study, we propose an effective framework based on Feature Pyramid Networks to improve the recognition accuracy of deep and shallow images while guaranteeing the recognition speed of PeleeNet structured images. Compared with PeleeNet, the accuracy of structure recognition on CIFA-10 data set increased by 4.0%.

• Philosophy of Education
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• no.66
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• pp.77-103
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• 2018

This study focused on the meaning of 'educational philosophy' by the method of corpus analysis. There is the difference of meaning on educational philosophy between professional researchers and publics. This semantic phenomenon implies that the image acoustics of 'educational philosophy' are not matched between two groups. This study, which originated from Saussure's linguistics theory, examined the semantics of educational philosophy in the . Unlike philosophical inquiry on education, the definition of educational philosophy, the general public use 'educational philosophy' like the connotation of secret of successful learning and child nurturing. Given the power of the media and the mass, these tendency could affect the meaning and definition of educational philosophy. Professional researchers should investigate these acoustic image from the sense of linguistic and educational approaches. These researches could contribute to clarify descriptive and normative meaning of the educational philosophy.

• ETRI Journal
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• v.23 no.2
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• pp.41-51
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• 2001

MPEG-7 standardization work has started with the aims of providing fundamental tools for describing multimedia contents. MPEG-7 defines the syntax and semantics of descriptors and description schemes so that they may be used as fundamental tools for multimedia content description. In this paper, we introduce a texture based image description and retrieval method, which is adopted as the homogeneous texture descriptor in the visual part of the MPEG-7 final committee draft. The current MPEG-7 homogeneous texture descriptor consists of the mean, the standard deviation value of an image, energy, and energy deviation values of Fourier transform of the image. These are extracted from partitioned frequency channels based on the human visual system (HVS). For reliable extraction of the texture descriptor, Radon transformation is employed. This is suitable for HVS behavior. We also introduce various matching methods; for example, intensity-invariant, rotation-invariant and/or scale-invariant matching. This technique retrieves relevant texture images when the user gives a querying texture image. In order to show the promising performance of the texture descriptor, we take the experimental results with the MPEG-7 test sets. Experimental results show that the MPEG-7 texture descriptor gives an efficient and effective retrieval rate. Furthermore, it gives fast feature extraction time for constructing the texture descriptor.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1219-1232
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• 2004

Recently, the web sites, such as e-business sites and shopping mall sites, deal with lots of catalog image information and contents. As a result, it is required to support semantic-based image retrieval efficiently on such image data. This paper presents a semantic-based image retrieval system, which adopts XML and Fuzzy technology. To support semantic-based retrieval on product catalog images containing multiple objects, we use a multi-level metadata structure which represents the product information and semantics of image data. To enable semantic-based retrieval on such image data, we design a XML database for storing the proposed metadata and study how to apply fuzzy data. This paper proposes a system, generate the fuzzy data automatically to use the image metadata, that can support semantic-based image retrieval by utilizing the generating fuzzy data. Therefore, it will contribute in improving the retrieval correctness and the user's satisfaction on semantic-based e-catalog image retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1676-1689
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• 2014

At present, the Content-Based Image Retrieval (CBIR) system has become a hot research topic in the computer vision field. In the CBIR system, the accurate extractions of low-level features can reduce the gaps between high-level semantics and improve retrieval precision. This paper puts forward a new retrieval method aiming at the problems of high computational complexities and low precision of global feature extraction algorithms. The establishment of the new retrieval method is on the basis of the SIFT and Harris (APISH) algorithm, and the salient region of interest points (SRIP) algorithm to satisfy users' interests in the specific targets of images. In the first place, by using the IPDSH and SRIP algorithms, we tested stable interest points and found salient regions. The interest points in the salient region were named as salient interest points. Secondary, we extracted the pseudo-Zernike moments of the salient interest points' neighborhood as the feature vectors. Finally, we calculated the similarities between query and database images. Finally, We conducted this experiment based on the Caltech-101 database. By studying the experiment, the results have shown that this new retrieval method can decrease the interference of unstable interest points in the regions of non-interests and improve the ratios of accuracy and recall.

• Cross-Cultural Studies
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• v.38
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• pp.461-500
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• 2015

This study aims to investigate the effective method to improve the competence of the Vocabulary by the image and listening towards the ELF. In the first part, we observed the problems and point improvement on learning vocabulary based on learner survey. In the second part, we analyzed two remarkable studies: - consistent and adapt method, communicational context - method based on the lexical, morphological semantical, notional and thematic field Then we proposed effective methods that are applicable to the vocabulary's learning in the class : - learning vocabulary by combining the words - learning vocabulary based on the meaning field - learning vocabulary as concrete characters - learning vocabulary by the descriptive character - learning vocabulary with the type "who am I?" - learning vocabulary by listening For teachers, one of the difficulties to the conduct of vocabulary course is that learners take passive position. Specifically, it is the teachers who play an important role because it runs in the direction of the course. However, learners do not show the active attitude for vocabulary lessons despite the course to take to improve their vocabulary skills. Therefore, teachers must prepare course materials that can both improve the competence of the vocabulary of learners and cause their interest or desire on the current vocabulary. This is why teachers should exploit various materials depending on the skill level of the learner vocabulary.

• Perspectives in Nursing Science
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• v.14 no.1
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• pp.32-36
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• 2017

Purpose: The purpose of this study is to analyze the factors influencing students' active practice in clinical nursing education and to find ways to improve them. Methods: In this study, the factors affecting students' practice in nursing clinical practice were reviewed and their opinions were discussed. Results: A limited clinical practice environment, lack of a role model of leading and independent nurse interferes with the active practice of nursing students and negatively affects the image of nurses. On the other hand, semantics that links practice to students'life supports active practice. Conclusion: Clinical practicum educators should actively carry out the role of introducing students to rational of their practice, and arrange meetings with nurses and students who carry out nursing care independently with leadership. In addition, the clinical nursing education should include courses that allow students to discuss the various issues they have in the nursing practice.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.92-98
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• 2024

The current software industry has an emerging issue with natural language-based requirement specifications. However, the accuracy of such requirement analysis remains a concern. It is noted that most errors still occur at the requirement specification stage. Defining and analyzing requirements based on natural language has become necessary. To address this issue, the linguistic theories of Chomsky and Fillmore are applied to the analysis of natural language-based requirements. This involves identifying the semantics of morphemes and nouns. Consequently, a mechanism was proposed for extracting object state designs and automatically generating code templates. Building on this mechanism, I suggest generating natural language-based comic images. Utilizing state diagrams, I apply changes to the states of comic characters (protagonists) and extract variations in their expressions. This introduces a novel approach to comic image generation. I anticipate highly productive comic creation by applying software processes to Cartoon ART.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.