• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.47-60
• /
• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of Korea Multimedia Society
• /
• v.12 no.11
• /
• pp.1640-1651
• /
• 2009

In this paper we introduce a new approach to stroke based rendering using brush stroke profile. Our proposed method, based on image retrieval method, is a simple but flexible and scalable method to create various painting styles, for which scalable database constructed with the collection of real stroke data is used. Input image is reproduced with combinations of brush stoke in the database, when a search process to determinate appropriate brush stroke and a judgment process to decide whether to draw the retrieved brush stroke on the canvas or not are presented. In addition, this paper suggests a new brush stroke model and a depiction technique in order to utilize effective height information which allows natural texture depiction, or good visual effect, without carrying out physical simulation. Our method is able to create diverse variations of painting by controling various user parameters. It also provides scalable framework that can produce various painting styles with different artistic media by changing the stroke combinations of stroke database.

• The Journal of Korean Association of Computer Education
• /
• v.7 no.5
• /
• pp.111-120
• /
• 2004

The MPEG-7 standard defines a set of descriptors that extracts low-level features such as color, texture and object shape from an image and generates metadata in order to represent these extracted information. But the matching performance for image mining ma y not be satisfactory by u sing only on e of these features. Rather than by combining these features we can achieve a better query performance. In this paper we propose a new image retrieval technique for image mining that combines the features extracted from MPEG-7 visual color and texture descriptors. Specifically, we use only some specifications of Scalable Color Descriptor (SCD) and Non-Homogeneous Texture Descriptor also known as Edge Histogram Descriptor (EHD) for the implementation of the color and edge histograms respectively. MPEG-7 standard defines $l_{1}$-norm based matching in EHD and SCD. But in our approach, for distance measurement, we achieve a better result by using cosine similarity coefficient for color histograms and Euclidean distance for edge histograms. Our approach toward this system is more experimental based than hypothetical.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.1-6
• /
• 2023

Morphological analysis is a branch of natural language processing, is now a rapidly growing field. The fundamental tenet of morphological analysis is that it can establish the roots or stems of words and enable comparison to the original term. Arabic is a highly inflected and derivational language and it has a strong structure. Each root or stem can have a large number of affixes attached to it due to the non-concatenative nature of Arabic morphology, increasing the number of possible inflected words that can be created. Accurate verb recognition and extraction are necessary nearly all issues in well-known study topics include Web Search, Information Retrieval, Machine Translation, Question Answering and so forth. in this work we have designed and implemented an algorithm to detect and recognize Arbic Verbs from Arabic text.The suggested technique was created with "Python" and the "pyqt5" visual package, allowing for quick modification and easy addition of new patterns. We employed 17 alternative patterns to represent all verbs in terms of singular, plural, masculine, and feminine pronouns as well as past, present, and imperative verb tenses. All of the verbs that matched these patterns were used when a verb has a root, and the outcomes were reliable. The approach is able to recognize all verbs with the same structure without requiring any alterations to the code or design. The verbs that are not recognized by our method have no antecedents in the Arabic roots. According to our work, the strategy can rapidly and precisely identify verbs with roots, but it cannot be used to identify verbs that are not in the Arabic language. We advise employing a hybrid approach that combines many principles as a result.

• Journal of KIISE:Databases
• /
• v.37 no.3
• /
• pp.127-136
• /
• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Korean Journal of Construction Engineering and Management
• /
• v.16 no.3
• /
• pp.113-122
• /
• 2015

VE workshop is performed based on VE expert' experiences without retrieving VE data of similar previous projects. Moreover, it usually omitted or applied for the sake of formality due to insufficiently understanding VE function, limited time, space and budget. Even though many studies have established VE databases for retrieving and reusing VE data, VE workshop is still inefficient and ineffective to improve projects' values. With this regard, this study proposes a preliminary prototype for improving VE workshop, which utilizes the state-of-the-art information communication technologies(ICTs) including Building Information Modeling(BIM), Mobile Computing(MC), Network Service System(NSS), and Database Management System(DBMS) for better managing, storing and reusing VE data. The prototype was developed to evaluate advantages and limitations. The results show that the proposed prototype can support visual VE data retrieval from similar previous projects, enhance communication among VE team and save much time and cost comparing to traditional VE. Through this, the productivity of VE workshop can improve efficiently and effectively.

• Journal of KIISE:Software and Applications
• /
• v.35 no.1
• /
• pp.1-11
• /
• 2008

This paper detected the characteristics of motion vector to support efficient content -based video search of video. Traditionally, the present frame of a video was divided into blocks of equal size and BMA (block matching algorithm) was used, which predicts the motion of each block in the reference frame on the time axis. However, BMA has several restrictions and vectors obtained by BMA are sometimes different from actual motions. To solve this problem, the foil search method was applied but this method is disadvantageous in that it has to make a large volume of calculation. Thus, as an alternative, the present study extracted the Spatio-Temporal characteristics of Motion Vector Spatio-Temporal Correlations (MVSTC). As a result, we could predict motion vectors more accurately using the motion vectors of neighboring blocks. However, because there are multiple reference block vectors, such additional information should be sent to the receiving end. Thus, we need to consider how to predict the motion characteristics of each block and how to define the appropriate scope of search. Based on the proposed algorithm, we examined motion prediction techniques for motion compensation and presented results of applying the techniques.

• KIPS Transactions on Software and Data Engineering
• /
• v.1 no.1
• /
• pp.55-68
• /
• 2012

In this paper, we propose a novel method to solve the problem of excessive segmentation out of the method of segmenting regions from an image using Homogeneity Threshold($H_T$). The algorithm of the previous image segmentation based on $H_T$ was carried out region growth by using only the center pixel of selected window. Therefore it was caused resulting in excessive segmented regions. However, before carrying region growth, the proposed method first of all finds out whether the selected window is homogeneity or not. Subsequently, if the selected window is homogeneity it carries out region growth using the total pixels of selected window. But if the selected window is not homogeneity, it carries out region growth using only the center pixel of selected window. So, the method can reduce remarkably the number of excessive segmented regions of image segmentation based on $H_T$. In order to show the validity of the proposed method, we carried out multiple experiments to compare the proposed method with previous method in same environment and conditions. As the results, the proposed method can reduce the number of segmented regions above 40% and doesn't make any difference in the quality of visual image when we compare with previous method. Especially, when we compare the image united with regions of descending order by size of segmented regions in experimentation with the previous method, even though the united image has regions more than 1,000, we can't recognize what the image means. However, in the proposed method, even though image is united by segmented regions less than 10, we can recognize what the image is. For these reason, we expect that the proposed method will be utilized in various fields, such as the extraction of objects, the retrieval of informations from the image, research for anatomy, biology, image visualization, and animation and so on.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.1-19
• /
• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Korean Journal of Occupational Health Nursing
• /
• v.3
• /
• pp.54-70
• /
• 1993

According as the automation of clerical work(OA ; Office Automation) develops, the use of VDT(Visual or Video Display Terminal) is increasing suddenly. But, in proportion to the spread of office automation(OA tendency), the self-conciousness syptom attendant upon the work is appearing also (Kim, Jung Tae, Lee, Young Ook, 1990). The apparatuses of office enable the clerical workers to be convenient and perform mass businesses. But, they are increasing the opportunity to be exposed to VDT syndrom, techno stress, computer terminal disease, pain by muscle strain(RSI), bradycausia of noise nature, and electromagnetic waves, etc. which are referred to as the new type of occupational diseases to the workers. It is the real situation that the workers to use VDT is complaining of the physical inconvenience sense in the recent newspaper and literature, it is the point of time that the sydrome to come from VDT use and computer terminal disease, etc. must be classified into the occupational disease(Lee, Kwang Young 1990, Lee, Kyoo Hak 1990, Lee, Won Ho 1991, Lee, Si Young 1991, Lee, Joon 1991, Choi, Young Tae 1991, Heo, Seung Ho 1989). In addition, it is the real situation that the scientifitic study result about the scope that electromagnetic waves has influence on the human body has not been suggested yet, and criticism on the stable exposure permission standard about electromagnetic waves to be emitted from VDT and on the problem in the health about electromagnetic waves is continuing. (IEEE Spectrum, 1990). In addition according to the experience of nursery business of industry field, it is the real situation that the patients who consult complaining of physical and mental inconvenience sence, among the users of apparatus of office automation, are reaching 10% of the patients coming to doctor's room. Therefore, it is necessary to confirm the self-consciousness symptom that the clerical workers complain of multilaterally with the actual state examination about the use of the apparatuses of offices automaton. Thus, this study was tried as th basic data for the cosultation and education for the maintenance and furtherance of the health of workers as the nurse of industry field, by confirming the contents of self-consciousness symptom attendant upon the use of the apparatus for office outomation making the financial institution in which the spparatus for office automation in most frequently used as the subject, and by examining whether there is the difference according to the subject of study, the data were collected, by using the questionnaire method, making 200 workers who consented to the study participation as the subject, among the persons who have spent over 3 months since they used the apparatuses for office automation and didn't receive the treatment in hospital due to the clerical disease for recent 3 years. The period of data collection was from Oct. 9, 1991 to Oct. 12. As for the measurement instrument about the complaint if self-consciousness symptom attendant upon the use of apparatuses fo office automation, the question item on the complaint symptom of health problem attendant upon the treatment of VDT that Kim(1991) developed and on CMI health problem and the question items on the fatigue degree due to industry were used by previous examination to 25 persons. Collected data were analyzed with the statistical method such as percentage, arithmetic mean, Person correlation coeffient, Kai square verfication, t-test, ANOVA, etc. by using SPSS/PC+ program, and the result is as follows : 1. The self-consciousness symptom that the clerical workers complained of most frequetly appeared high in 'My eyes are tired'(99.4%), 'I feel fatigue and weariness'(99.4%), 'I feel that my head is heavy5(90.0%), 'eyesight fell'(88.8%), 'I have a stiff neck'(88.8%), 'I fell pain in the shoulder'(85.0%), 'I feel cold and painful in the eyes'(76.9%), 'I feel the dry sense of eyeball'(76.2%), 'My nerves are edgy, and I an fretful, (75.6%), 'I feel pain in the waist'(73.2%) and 'I fell pain in the back'(72.8%). It emerged that the subject use the apparatuses for office automation complained of self-consciousness symptoms related to visual symptoms and musculoskeletal symptoms. 2. As for the general feature of examination subjects, the result to see the distribution by classifying into sex, age, school career, use career of apparatuses for office automation, skillfulness degree of the use of apparatus for office automation, use hours of the apparatuses for office automation per 1 day, type of business of the apparatus for office automation, rest hours during the use of apparatus for office automation, satifaction degree of business of office automation, and work circumstance, etc. emerged as follows : As for the sex of subjects, the distribution showed that men were 58.8% and women were 41.3%, Age was average 26.9. As the distribution of school career, the distribution showed that4below the graduation of high school' was 58.8%, 'graduation from junior college-university' was 35.0%, and 'over graduate school' was 6.3%. In the question to ask the existence or non-existence of experience of health consultation in connection with the work of office automation, the response that I had the consultation exprience and I feel the necessity emergerd as 90.1% And, the case that the subject who didn't wear the glasses or lens before using the OA apparatus wear glasses or lens after using OA apparatus emerged as 28.3% of whole. As for the existence or non-existence of use career of OA apparatus, the case under 3 years was highest as 52. 7%. As for the skillfulnness degree about the use of apparatus for office automation, most of them are skillful with the fact that 'common' was 44.4%, 'skill' was 42.5%, and 'unskillful' was 13.1% As for the use average hours of the apparatus for office automation per 1 day, the distribution showed that the case under 3-6 hours was 33.1%, the case under 6-9 hours was 28.1%, the case under 3 hours was 30.6%, and the case over 9 hours was 8.1% Main OA business and the use hours for 1 day showed in the order of keeping and retrieval, business of information transmission(162min), business of information transmission(79.3 min), business of document framing(55.5 min), and business of duplication and printing(25.4min). as for the rest during the use of apparatus for affice automation, that I take rest occasion demands the major portion, but that I take after completing the work emerged as 33.8%. Though the subiness gets to be convenient by the use of the apparatus for of office automation, respondents who showed the dissatisfaction about the present OA business emergd high as 78.1%. The work circumstances of each office was good with the fact that the temperature of office was 21.8, noise was average 42.7db, and the illumination was average 364.4 lx, in the light of ANSi/HFS 100 Standard. 3. Sight syptom, musculoskeletal symptom, skin and other symptoms showed the significant difference according to the extent of skillfulness of the apparatus for office automation. All the symptoms exept skin symptom showed the difference according to the use hours of the apparatus for office automation. All the question items exept the sytoms of digestive organs and the rest hours during the apparatus for office automation showed the signicant difference. The question item which showed the signicant difference from the satisfaction degree of present OA business showed the significant difference from all the question item classified into 6 groups. But, age and school career didn't significant difference from the complaint of any self-consciousness symptoms.