A SOM-Based Information Organizer for Text and Video Data
Kenji Hatano, Qing Qian, Katsumi Tanaka · 1997
We propose an information organizer for effective clustering and similarity-based retrieval of text and video data. Instead of giving keywords or authoring them, we use a vector space model and DCT image coding in order to extract characteristics of data. Data are clustered by Kohonen's self-organizing map, and the result is visualized in a 3D form. By this, similarity-based retrieval is achieved. We implemented a prototype system and report experimental results. We consider that our system effectively promotes reuse of distributed text and image data assets. Keywords Self Organizing Map, data classification, browsing and asset retrieval. 1 Introduction Hypertext has become a highly effective and efficient information browser for wide-spread distributed information over global network. The techniques become more and more crucial as the electronically stored documents and multimedia data increase explosively. The node-link network model of hypertext is a simple and comprehensive mode...