Similarity retrieval based on self-organizing maps
- Authors
- Im, D.J.; Lee, M.; Lee, Y.K.; Kim, T.E.; Lee, S.; Lee, J.; Lee, K.K.; Cho, K.D.
- Issue Date
- May-2005
- Publisher
- SPRINGER-VERLAG BERLIN
- Keywords
- self-organizing maps; image databases; similarity retrieval; content-based image retrieval
- Citation
- COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, PT 2, v.3481, pp 474 - 482
- Pages
- 9
- Journal Title
- COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, PT 2
- Volume
- 3481
- Start Page
- 474
- End Page
- 482
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65514
- ISSN
- 0302-9743
1611-3349
- Abstract
- The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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