A Novel Approach for Filtering Junk Images from Google Search Results

Abstract

Keyword-based image search engines such as Google Images are now very popular for accessing large amount of images on the Internet. Because only the text information that are directly or indirectly linked to the images are used for image indexing and retrieval, most existing image search engines such as Google Images may return large amount of junk images which are irrelevant to the given queries. To filter out the junk images from Google Images, we have developed a kernel-based image clustering technique to partition the images returned by Google Images into multiple visually-similar clusters. In addition, users are allowed to input their feedbacks for updating the underlying kernels to achieve more accurate characterization of the diversity of visual similarities between the images. To help users assess the goodness of image kernels and the relevance between the returned images, a novel framework is developed to achieve more intuitive visualization of large amount of returned images according to their visual similarity. Experiments on diverse queries on Google Images have shown that our proposed algorithm can filter out the junk images effectively. Online demo is also released for public evaluation at: http://www.cs.uncc.edu/~jfan/google-demo/.