Because image database is very huge

the feature extraction and retrieval process are usually time consuming. In order to effectively use existing text information retrieval methods in content based image retrieval

especially the index mechanism of the product tf * idf by term frequency (tf) and inverse document frequency (idf) for each text document

this paper cooperates tf * idf model with triangle tree to improve the retrieval performance. First

after pixel-based histogram features of sub-block in certain image class are mapped to color concept space through independent component analysis (ICA)

we would obtain all of independent keyblock of such image class; then well-trained fuzzy support vector machine is used to recognize all of independent keyblocks contained by each image. Similar to text retrieval

in which the whole text document is indexed by

the recognized independent keyblock is used to index each image in database. Because independent component features are naturally high order independent with each other

compared to principle component analysis (PCA) method

this algorithm achieves higher performance. At last

triangle tree is used to hierachically index image database and thereof speed up retrieval.