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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 17B, Issue 6 - Dec 2010
Volume 17B, Issue 5 - Oct 2010
Volume 17B, Issue 4 - Aug 2010
Volume 17B, Issue 3 - Jun 2010
Volume 17B, Issue 2 - Apr 2010
Volume 17B, Issue 1 - Feb 2010
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Emotion-Based Music Retrieval Using Consistency Principle and Multi-Query Feedback
Shin, Song-Yi ; Park, En-Jong ; Eum, Kyoung-Bae ; Lee, Joon-Whoan ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 99~106
DOI : 10.3745/KIPSTB.2010.17B.2.099
In this paper, we propose the construction of multi-queries and consistency principle for the user`s emotion-based music retrieval system. The features used in the system are MPEG-7 audio descriptors, which are international standards recommended for content-based audio retrievals. In addition we propose the method to determine the weight that represent the importance of each descriptor for each emotion in order to reduce the computation. Also, the proposed retrieval algorithm that uses the relevance feedback based on consistency principal and multi-queries improves the success ratio of musics corresponding to user`s emotion.
Musical Instrument Recognition for the Categorization of UCC Music Source
Kwon, Soon-Il ; Park, Wan-Joo ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 107~114
DOI : 10.3745/KIPSTB.2010.17B.2.107
A guitar, a piano, and a violin are popular musical instruments for User Created Contents(UCC). However the patterns of audio signal generated by a guitar and a piano are too similar to differentiate. The difference between two musical instruments can be found by analyzing the frequency variation per each band near signal peaks. The distribution of probability on the existence of signal peaks based on Cumulative Histogram were applied to musical instrument recognition. Experiments with statistical models of the frequency variation per each band near signal peaks showed the 14% improvement of musical instrument recognition.
A Stereo Matching Technique using Multi-directional Scan-line Optimization and Reliability-based Hole-filling
Baek, Seung-Hae ; Park, Soon-Young ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 115~124
DOI : 10.3745/KIPSTB.2010.17B.2.115
Stereo matching techniques are categorized in two major schemes, local and global matching techniques. In global matching schemes, several investigations are introduced, where cost accumulation is performed in multiple matching lines. In this paper, we introduce a new multi-line stereo matching techniques which expands a conventional single-line matching scheme to multiple one. Matching cost is based on simple normalized cross correlation. We expand the scan-line optimization technique to a multi-line scan-line optimization technique. The proposed technique first generates a reliability image, which is iteratively updated based on the previous reliability measure. After some number of iterations, the reliability image is completed by a hole-filling algorithm. The hole-filling algorithm introduces a disparity score table which records the disparity score of the current pixel. The disparity of an empty pixel is determined by comparing the scores of the neighboring pixels. The proposed technique is tested using the Middlebury and CMU stereo images. The error analysis shows that the proposed matching technique yields better performance than using conventional global matching algorithm.
Research and Development of Document Recognition System for Utilizing Image Data
Kwag, Hee-Kue ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 125~138
DOI : 10.3745/KIPSTB.2010.17B.2.125
The purpose of this research is to enhance document recognition system which is essential for developing full-text retrieval system of the document image data stored in the digital library of a public institution. To achieve this purpose, the main tasks of this research are: 1) analyzing the document image data and then developing its image preprocessing technology and document structure analysis one, 2) building its specialized knowledge base consisting of document layout and property, character model and word dictionary, respectively. In addition, developing the management tool of this knowledge base, the document recognition system is able to handle the various types of the document image data. Currently, we developed the prototype system of document recognition which is combined with the specialized knowledge base and the library of document structure analysis, respectively, adapted for the document image data housed in National Archives of Korea. With the results of this research, we plan to build up the test-bed and estimate the performance of document recognition system to maximize the utilization of full-text retrieval system.
Reinforcement Post-Processing and Feedback Algorithm for Optimal Combination in Bottom-Up Hierarchical Classification
Choi, Yun-Jeong ; Park, Seung-Soo ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 139~148
DOI : 10.3745/KIPSTB.2010.17B.2.139
This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.
Selective Mutation for Performance Improvement of Genetic Algorithms
Jung, Sung-Hoon ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 149~156
DOI : 10.3745/KIPSTB.2010.17B.2.149
Since the premature convergence phenomenon of genetic algorithms (GAs) degrades the performances of GAs significantly, solving this problem provides a lot of effects to the performances of GAs. In this paper, we propose a selective mutation method in order to improve the performances of GAs by alleviating this phenomenon. In the selective mutation, individuals are additionally mutated at the specific region according to their ranks. From this selective mutation, individuals with low ranks are changed a lot and those with high ranks are changed small in the phenotype. Finally, some good individuals search around them in detail and the other individuals have more chances to search new areas. This results in enhancing the performances of GAs through alleviating of the premature convergence phenomenon. We measured the performances of our method with four typical function optimization problems. It was found from experiments that our proposed method considerably improved the performances of GAs.
Distributed Genetic Algorithm using Automatic Migration Control
Lee, Hyun-Jung ; Na, Yong-Chan ; Yang, Ji-Hoon ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 157~162
DOI : 10.3745/KIPSTB.2010.17B.2.157
We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithms is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets.
A Linguistic Case-based Fuzzy Reasoning based on SPMF
Choi, Dae-Young ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 163~168
DOI : 10.3745/KIPSTB.2010.17B.2.163
A linguistic case-based fuzzy reasoning (LCBFR) based on standardized parametric membership functions (SPMF) is proposed. It provides an efficient mechanism for a fuzzy reasoning within linear time complexity. Thus, it can be used to improve the speed of fuzzy reasoning. In the process of LCBFR, linguistic case indexing and retrieval based on SPMF is suggested. It can be processed relatively fast compared to the previous linguistic approximation methods. From the engineering viewpoint, it may be a valuable advantage.
Color Transformation of Images based on Emotion Using Interactive Genetic Algorithm
Woo, Hye-Yoon ; Kang, Hang-Bong ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 169~176
DOI : 10.3745/KIPSTB.2010.17B.2.169
This paper proposes color transformation of images based on user`s preference. Traditional color transformation method transforms only hue based on existing templates that define range of harmonious hue. It does not change saturation and intensity. Users would appreciate the resulting images that adjusted unnatural hue of images. Since color is closely related to peoples` emotion, we can enhance interaction of emotion-based contents and technologies. Therefore, in this paper, we define the range of color of each emotion for the transformation of color and perform the transformation of hue, saturation and intensity. However, the relationship of color and emotion depends on the culture and environment. To reflect these characteristics in color transformation, we propose the transformation of color that is based on user`s preference and as a result, people would be more satisfied. We adopt interactive genetic algorithm to learn about user`s preference. We surveyed the subject to analyze user`s satisfaction about transformed images that are based on preference, and we found that people prefer transformed images to original images. Therefore, we conclude that people are more satisfied with the transformation of the templates which reflected user`s preference than the one that did not.
Document Clustering Method using PCA and Fuzzy Association
Park, Sun ; An, Dong-Un ;
The KIPS Transactions:PartB, volume 17B, issue 2, 2010, Pages 177~182
DOI : 10.3745/KIPSTB.2010.17B.2.177
This paper proposes a new document clustering method using PCA and fuzzy association. The proposed method can represent an inherent structure of document clusters better since it select the cluster label and terms of representing cluster by semantic features based on PCA. Also it can improve the quality of document clustering because the clustered documents by using fuzzy association values distinguish well dissimilar documents in clusters. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.