Kun Seok Oh

Publications (5)0.44 Total impact

• Article: Image Retrieval by Edge Features Using Higher Order Autocorrelation in a SOM Environment

  Zaher AGHBARI, Kun Seok OH, Akifumi MAKINOUCHI
  02/2013;
• Article: Development of a social interaction questionnaire for the trainers and mothers of children with disabilities participating in Dousa-hou (Japanese psycho-rehabilitation) camps.

  Surender Kumar, Yong Seob Kim, Kun Seok Oh
  [show abstract] [hide abstract]
  ABSTRACT: A 12-item Social Interaction Questionnaire was developed to measure the social interactions among trainers and mothers of children with disabilities in Dousa-hou camps. Dousa-hou is a Japanese psychological rehabilitation method which is widely used for children with mental retardation, cerebral palsy, and autism in Japan and other Asian countries. The primary focus of the rehabilitation method is to improve bodily movements, posture, and social support to patients and their first-degree relatives as well as promoting social interaction among participants. Two factors of interaction, (1) educational and daily life matters and (2) health and care matters, emerged through factor analysis. Cronbach coefficient alpha of the questionnaire was .91. The back-translated version of the Social Interaction Questionnaire also yielded two factors and Cronbach coefficient alpha of .87. It was found that mothers or first degree relatives (N=138; M = 43.5 yr., SD = 12.3) of the patients reported more social interaction than trainers when interacting with their child's trainer, supervisor, other trainers, and other mothers during six-day Dousa-hou camps.
  Psychological Reports 11/2006; 99(2):591-8. · 0.44 Impact Factor
• Conference Proceeding: SOM-Based K-Nearest Neighbors Search in Large Image Databases.

  Zaher Aghbari, Kun Seok Oh, Yaokai Feng, Akifumi Makinouchi
  Visual and Multimedia Information Management, IFIP TC2/WG2.6 Sixth Working Conference on Visual Database Systems, May 29-31, 2002, Brisbane, Australia; 01/2002
• Source

  Available from: Kunihiko Kaneko

  Conference Proceeding: SOM-based R*-tree for similarity retrieval

  Kun-Seok Oh, Yaokai Feng, K. Kaneko, A. Makinouchi, Sang-Hyun Bae
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  ABSTRACT: Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e.g., documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors. A feature vector is a vector that represents a set of features, and are usually high-dimensional data. The performance of conventional multidimensional data structures (e.g., R-tree family K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R*-tree is the most successful variant of the R-tree. We propose a SOM-based R*-tree as a new indexing method for high-dimensional feature vectors. The SOM-based R*-tree combines SOM and R*-tree to achieve search performance more scalable to high dimensionalities. Self-organizing maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. We experimentally compare the retrieval time cost of a SOM-based R*-tree with that of an SOM and an R*-tree using color feature vectors extracted from 40,000 images.
  Database Systems for Advanced Applications, 2001. Proceedings. Seventh International Conference on; 02/2001
• Conference Proceeding: SOM-Based R*-tree for Similarity Retrieval.

  Kun Seok Oh, Yaokai Feng, Kunihiko Kaneko, Akifumi Makinouchi, Sang-Hyun Bae
  Database Systems for Advanced Applications, Proceedings of the 7th International Conference on Database Systems for Advanced Applications (DASFAA 2001), 18-20 April 2001 - Hong Kong, China; 01/2001