Management of image database with self-organizing map

Pao Yue-kong Library Electronic Theses Database

Management of image database with self-organizing map

 

Author: Lei, Tong
Title: Management of image database with self-organizing map
Degree: M.Sc.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Self-organizing maps.
Neural networks (Computer science)
Image processing -- Digital techniques.
Database management.
Department: Dept. of Electronic and Information Engineering
Pages: x, 84 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2303064
URI: http://theses.lib.polyu.edu.hk/handle/200/4704
Abstract: This dissertation presents management of image database with Self-Organizing Map. Two main contributions are reported: (1) A hybrid system combines image pre-classification and Self-Organizing Map modules for image classification management; (2) A new mixed training method based on Self-Organizing Map for map optimization under non-stationary environment. The hybrid system is designed to firstly classify the images in the database into attentive and non-attentive two categories, and then adaptively extract targeted features from the images with specific properties. Finally, Self-Organizing Map (SOM) is used to cluster the images' features within each category individually so as to implement the image classification management based on content. The conventional Self-Organizing Map can only be trained under stationary environment, in which the dataset has to be fixed if the training process begins, while the map structure cannot be changed once the training process ends. However, in practice trie database may not be permanently invariant for applications, for example, when the training dataset is enlarged, and the map often needs to be adaptively changed. The conventional SOM system cannot simply alter the nodes distribution in the output layer of the map unless destroying the old prototype and training a new one; while if the training dataset contains lots of samples, the whole process will be very time consuming. In this thesis, we propose a new mixed training method that provides a fast algorithm for optimizing the nodes distribution in SOM according to the new coming data.

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