• Title/Summary/Keyword: Time-based Clustering

Search Result 716, Processing Time 0.025 seconds

Color-related Query Processing for Intelligent E-Commerce Search (지능형 검색엔진을 위한 색상 질의 처리 방안)

  • Hong, Jung A;Koo, Kyo Jung;Cha, Ji Won;Seo, Ah Jeong;Yeo, Un Yeong;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
    • /
    • v.25 no.1
    • /
    • pp.109-125
    • /
    • 2019
  • As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

Case Analysis of the Promotion Methodologies in the Smart Exhibition Environment (스마트 전시 환경에서 프로모션 적용 사례 및 분석)

  • Moon, Hyun Sil;Kim, Nam Hee;Kim, Jae Kyeong
    • Journal of Intelligence and Information Systems
    • /
    • v.18 no.3
    • /
    • pp.171-183
    • /
    • 2012
  • In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

A Grouping Method of Photographic Advertisement Information Based on the Efficient Combination of Features (특징의 효과적 병합에 의한 광고영상정보의 분류 기법)

  • Jeong, Jae-Kyong;Jeon, Byeung-Woo
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.48 no.2
    • /
    • pp.66-77
    • /
    • 2011
  • We propose a framework for grouping photographic advertising images that employs a hierarchical indexing scheme based on efficient feature combinations. The study provides one specific application of effective tools for monitoring photographic advertising information through online and offline channels. Specifically, it develops a preprocessor for advertising image information tracking. We consider both global features that contain general information on the overall image and local features that are based on local image characteristics. The developed local features are invariant under image rotation and scale, the addition of noise, and change in illumination. Thus, they successfully achieve reliable matching between different views of a scene across affine transformations and exhibit high accuracy in the search for matched pairs of identical images. The method works with global features in advance to organize coarse clusters that consist of several image groups among the image data and then executes fine matching with local features within each cluster to construct elaborate clusters that are separated by identical image groups. In order to decrease the computational time, we apply a conventional clustering method to group images together that are similar in their global characteristics in order to overcome the drawback of excessive time for fine matching time by using local features between identical images.

Moving Object Tracking using Query Relaying in Wireless Sensor Networks (무선 센서 네트워크에서 질의 중계를 이용한 이동 객체의 위치 추적 방안)

  • Kim, Sangdae;Kim, Cheonyong;Cho, Hyunchong;Yim, Yongbin;Kim, Sang-Ha
    • KIISE Transactions on Computing Practices
    • /
    • v.20 no.11
    • /
    • pp.598-603
    • /
    • 2014
  • In wireless sensor networks, two methods have been generally used to track continuously moving object: a user query-based method and a periodic report-based method. Although the former method generates more overhead as a result of the user queries, the former one is also an energy-efficient method that does not transfer unnecessary information. For the user query-based method, a virtual tree that consist of sensor nodes is used to perform the user query and the sensor reporting. The tree stores the information of the mobile objects, and the stored information triggers a report b the user query. However, in case of a fast-moving object, the tracking accuracy decreases as a result of the time delay of the end-to-end repeated query. In order to solve this problem, we propose a query-relay method that reduces the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to adjacent nodes according to the movement of mobile object that is tracked. When the query messages are relayed. The end-to-end querying time delay is reduced. and a simulation shows that our method is superior to existing ones in terms of tracking accuracy.

A Design on Face Recognition System Based on pRBFNNs by Obtaining Real Time Image (실시간 이미지 획득을 통한 pRBFNNs 기반 얼굴인식 시스템 설계)

  • Oh, Sung-Kwun;Seok, Jin-Wook;Kim, Ki-Sang;Kim, Hyun-Ki
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.16 no.12
    • /
    • pp.1150-1158
    • /
    • 2010
  • In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

Improvement of MODIS land cover classification over the Asia-Oceania region (아시아-오세아니아 지역의 MODIS 지면피복분류 개선)

  • Park, Ji-Yeol;Suh, Myoung-Seok
    • Korean Journal of Remote Sensing
    • /
    • v.31 no.2
    • /
    • pp.51-64
    • /
    • 2015
  • We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

Tumor Habitat Analysis Using Longitudinal Physiological MRI to Predict Tumor Recurrence After Stereotactic Radiosurgery for Brain Metastasis

  • Da Hyun Lee;Ji Eun Park;NakYoung Kim;Seo Young Park;Young-Hoon Kim;Young Hyun Cho;Jeong Hoon Kim;Ho Sung Kim
    • Korean Journal of Radiology
    • /
    • v.24 no.3
    • /
    • pp.235-246
    • /
    • 2023
  • Objective: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. Materials and Methods: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. Results: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). Conclusion: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

Evaluation of Water Quality for the Han River Tributaries Using Multivariate Analysis (다변량 통계 분석기법을 이용한 한강수계 지천의 수질 평가)

  • Kim, Yo-Yong;Lee, Si-Jin
    • Journal of Korean Society of Environmental Engineers
    • /
    • v.33 no.7
    • /
    • pp.501-510
    • /
    • 2011
  • In this study, water pollution sources of 14 major tributaries of Han river and characteristics of water quality for each target streams were evaluated based on water quality data in 2007.1-2009.12 (14 data sets) using a statistical package, SPSS-17.0. Cluster analysis over time and space for each stream resulted in 4 groups for the spatial variations in which type and density of pollution sources in the basins showed the greatest impact on grouping. Moreover, cluster analysis for the time variation in which rainfall, temperature and eutrophication were shown to contribute to the clustering, produced 2 groups, from summer to fall (July-Oct.) and from winter to early summer (Nov.-June). Four factors were found as responsible for the data structure explaining 71-90% of the total variance of the data set depending on the streams and they were organic matter, nutrients, bacterial contamination. Factor analysis showed main factors (water pollutants) changed according to the season with different pattern for each stream. This study demonstrated that water quality of each stream could produce useful outcomes when factor and pollution source of basin were evaluated together.

Charging Schedule Establishment of PEVs considering Power System Constraints (전력계통 제약을 고려한 플러그인 전기자동차 충전계획 수립)

  • Gwon, Han Na;Kook, Kyung Soo
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.19 no.5
    • /
    • pp.632-639
    • /
    • 2018
  • Recently, a policy has been enforced to supply Plug-in Electric Vehicles (PEVs) but this may require reinforcement of the power system depending on its clustering because PEVs are charged directly from power systems. On the other hand, as the reinforcement of power system is limited by time and budget, it is important to supply the charging demand of PEVs efficiently using the existing power systems to increase the diffusion of PEVs. This paper establishes a charging schedule for Plug-in Electric Vehicles (PEVs) considering the power system constraints. For this, the required amount and time of the charging demand for an individual PEV was modeled to integrate into power systems based on the driving pattern and charging tariff of PEV. Furthermore, the charging schedule of PEVs was established to meet the power system constraints by calculating the operating conditions of the power systems with PEVs.

Automatic Detection of Foreign Body through Template Matching in Industrial CT Volume Data (산업용 CT 볼륨데이터에서 템플릿 매칭을 통한 이물질 자동 검출)

  • Ji, Hye-Rim;Hong, Helen
    • Journal of Korea Multimedia Society
    • /
    • v.16 no.12
    • /
    • pp.1376-1384
    • /
    • 2013
  • In this paper, we propose an automaticdetection method of foreign bodies through template matching in industrial CT volume data. Our method is composed of three main steps. First,Indown-sampling data, the product region is separated from background after noise reduction and initial foreign-body candidates are extracted using mean and standard deviation of the product region. Then foreign-body candidates are extracted using K-means clustering. Second, the foreign body with different intensity of product region is detected using template matching. At this time, the template matching is performed by evaluating SSD orjoint entropy according to the size of detected foreign-body candidates. Third, to improve thedetection rate of foreign body in original volume data, final foreign bodiesare detected using percolation method. For the performance evaluation of our method, industrial CT volume data and simulation data are used. Then visual inspection and accuracy assessment are performed and processing time is measured. For accuracy assessment, density-based detection method is used as comparative method and Dice's coefficient is measured.