Using Keyblock Statistics to Model Image Retrieval

Keyblock, which is a new framework we proposed for the contentbased image retrieval, is a generalization of the text-based information retrieval technology in the image domain. In this framework, keyblocks, which are analogous to keywords in text document retrieval, can be constructed by exploiting a clustering approach. Then an image can be represented as a list of keyblocks similar to a text document which can be considered as a list of keywords. Based on this image representation, various feature models can be constructed for supporting image retrieval. In this paper, we will conduct keyblock statistic analysis and propose keyblock importance vector to improve the retrieval performance. The statistic analysis is based on the keyblock entropy as well as the keyblock frequency in the image database.