In this paper, we address a ranking problem in web image
retrieval. Due to the growing availability of web images, comprehensive
retrieval of web images has been expected. Conventional systems for web
image retrieval are based on keyword- based retrieval. However, we often
find undesirable retrieval results from the keyword based web image
retrieval system since the system uses the limited and inaccurate text information
of web images ; a typical system uses text information such as
surrounding texts and/or image filenames, etc. To alleviate this situation,
we propose a new ranking approach which is the integration of results
of text and image content via analyzing the retrieved results. We define
four ranking methods based on the image contents analysis of the retrieved
images; (1) majority-first method, (2) centroid-of-all method, (3)
centroid-of-top K method, and (4) centroid-of-largest-cluster method.
We evaluate the retrieval performance of our methods and conventional
one using precision and recall graphs. The experimental results show
that the proposed methods are more effective than conventional keywordbased
retrieval methods.