Journal of Intelligence and Information Systems,
Vol. 15, No. 2, June 2009
Adaptive Scene Classification based on Semantic Concepts and Edge Detection
Nuraini Jamil, Shohel Ahmed, Kim Kangseok, and Kang Sanggil
Vol. 15, No. 2, Page: 1 ~ 13
Scene classification and concept-based procedures have been the great interest for image categorization applications for large database. Knowing the category to which scene belongs, we can filter out uninterested images when we try to search a specific scene category such as beach, mountain, forest and field from database. In this paper, we propose an adaptive segmentation method for real-world natural scene classification based on a semantic modeling. Semantic modeling stands for the classification of sub-regions into semantic concepts such as grass, water and sky. Our adaptive segmentation method utilizes the edge detection to split an image into sub-regions. Frequency of occurrences of these semantic concepts represents the information of the image and classifies it to the scene categories. K-Nearest Neighbor (k-NN) algorithm is also applied as a classifier. The empirical results demonstrate that the proposed adaptive segmentation method outperforms the Vogel and Schiele's method in terms of accuracy.
Minimizing the Risk of an Open Computing Environment Using the MAD Portfolio Optimization
Hak Jin Kim, and Jihyoun Park
Vol. 15, No. 2, Page: 15 ~ 31
Keywords : Open Computing Environment, Grid Computing, Service Level Agreement, Risk Management, Portfolio Optimization, Simulation
The next generation IT environment is expected to be an open computing environment based on Grid computing technologies, which allow users to access to any type of computing resources through networks. The open computing environment has benefits in aspects of resource utilization, collaboration, flexibility and cost reduction. Due to the variation in performance of open computing resources, however, resource allocation simply based on users' budget and time constraints often fails to meet the Service Level Agreement(SLA). This paper proposes the Mean-Absolute Deviation(MAD) portfolio optimization approach, in which service brokers consider the uncertainty of performance of resources, and compose resource portfolios that minimize the uncertainty. In order to investigate the effect of this approach, we simulate an open computing environment with varying uncertainty levels, users' constraints, and brokers' optimization strategies. The simulation result concludes threefolds. First, the MAD portfolio optimization improves the success ratio of delivering the required performance to users. Second, the success ratio depends on the accuracy in predicting the variability of performance. Thirdly, the measured variability can also help service brokers expand their service to cost-critical users by discounting the access cost of open computing resources.
Intelligent Diagnosis Assistant System of Capsule Endoscopy Video Through Analysis of Video Frames
Hyun Gyu Lee, Min Kuk Choi, Don Haeng Lee, and Sang Chul Lee
Vol. 15, No. 2, Page: 33 ~ 48
Keywords : Capsule Endoscope, Image Similarity, Image Entropy
Capsule endoscopy is one of the most remarkable inventions in last ten years. Causing less pain for patients, diagnosis for entire digestive system has been considered as a most convenience method over a normal endoscope. However, it is known that the diagnosis process typically requires very long inspection time for clinical experts because of considerably many duplicate images of same areas in human digestive system due to uncontrollable movement of a capsule endoscope. In this paper, we propose a method for clinical diagnosticians to get highly valuable information from capsule-endoscopy video. Our software system consists of three global maps, such as movement map, characteristic map, and brightness map, in temporal domain for entire sequence of the input video. The movement map can be used for effectively removing duplicated adjacent images. The characteristic and brightness maps provide frame content analyses that can be quickly used for segmenting regions or locating some features(such as blood) in the stream. Our experiments show the results of four patients having different health conditions. The result maps clearly capture the movements and characteristics from the image frames. Our method may help the diagnosticians quickly search the locations of lesion, bleeding, or some other interesting areas.
Improved SIM Algorithm for Contents-based Image Retrieval
Kwang Baek Kim
Vol. 15, No. 2, Page: 49 ~ 59
Keywords : Context-based, Image Retrieval, SIM, Multi-layered SOM Learning
Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.
Study for Blog Clustering Method Based on Similarity of Titles
Ki Jun Lee, Myung Jin Lee, and Woo Ju Kim
Vol. 15, No. 2, Page: 61 ~ 74
Keywords : Search, Blog Search, Clustering
With an exponential growth of blogs, lots of important data have appeared on blogs. However, since main topics mentioned in blog pages are quite different from general web pages, there are problems which can't be solved by general search engines. Therefore, many researchers have studied searching methods only for blogs to help users who want to have useful information on blog. We also present a blog classifying method based on similarity of titles. First, we analyze blogs and blog search engines to find problems and solution of current blog search. Second, applying our similarity algorithm on blog titles, we discuss a way to develop clustering method only for blog. Finally, by making a prototype system of our algorithm, we evaluate our algorithm's effectiveness and show conclusion and future work. We expect this algorithm could add its power to current search engine.
Design and Analysis of Social Network Service Model Using a Ubiquitous Business Card
Jae Suhp Oh, and Kyoung Jun Lee
Vol. 15, No. 2, Page: 75 ~ 95
Keywords : u-Business Card, u-Social Network Service, Ubiquitous Computing, Service Model, NFC(Near Field Communication)
The aim of this research is to design and analyze a social network service model using mobile RFID based business card. This paper suggests how the behavior of exchanging business cards will be changed in ubiquitous environment and designs a social networking service model using a ubiquitous business card, which embeds a RFID tag. We describe the scenarios and analyze a role, value and potential benefits of participants of the u-SNS service model. For the proof of the superiority and the feasibility of our model, we compare it with its related researches and products based on the calculation of the benefits and costs of the alternatives.
Model Based Approach to Estimating Privacy Concerns for Context-Aware Services
Yonnim Lee, and Ohbyung Kwon
Vol. 15, No. 2, Page: 97 ~ 111
Keywords : Context Aware Service, Privacy Concern, Privacy Concern Prediction
Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

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