在實作中，我們利用自助式建構法做關鍵字延伸達到圖片分類的目的，有別於一般的字義式關鍵字延伸，自助式建構法可以做到聯想式的關鍵字延伸，利用這樣的關鍵字延伸機制，可以做到WordNet等無法做到的圖像分類效果。圖像分類訓練方面，採用的是特徵鑑別公制的概念，即是先將特徵依照屬性作分類，再依照特徵的分類結果，將圖像作重新的分類。藉由自助式建構法與特徵鑑別公制兩者間的運用，讓系統對圖像的分類效能更逼近使用者手工分類的結果。 In this paper, we have designed a database that can automatically classify images, for the purpose of sorting through a large number of images more conveniently and thus save manpower and resources.
This database is characterized by high level features (text-based) to image classifying. Its features include: extending a keyword through bootstrapping construction. First of all bootstrapping construction method extended words that the user manually inputted, and then increased the value and number of classificatory keywords. The keywords and classificatory keywords after extension underwent similarity value calculations. Finishing this step results in an initial classifying for images, and the step is repeated until there are no more changes in the classifications.
Whereas common ways of extending a keyword deal with its definition, bootstrapping construction allows expansion through associative extension. This type of keyword expansion mechanism is capable of classifying images in ways that WordNet cannot. Aside from using bootstrapping construction to expand keywords and to classify images, we have also added a discriminative feature metric to increase the precision and recall rates of image classifying to our standards.