Abstract
Image database of the butterfly specimen is constructed using Self-Organizing Maps (SOM). Input vector for SOM consists of color, shape, and texture characteristics. Color component is a 10-dimensional vector of a HSL color histogram. Shape component is a combination of a 24-dimensional vector of local average radii and a 2-dimensional vector of momenta. Texture component is a 52-dimensional vector of several statistical quantities such as angular second moment, contrast, correlation and entropy. As a result, the images each expressed by 88-dimensional feature vector are effectively classified to form several categories. At the retrieval, not a character-based keyword but an image is used as a query to search the most similar image stored in the database. For the user without an appropriate input image, color and texture sample palettes, and shape templates are given to a user, where the templates are obtained by the data clustering using 1-dimensional SOM.
