Advanced feature extraction for Keyblock-based image retrieval

New models named N-block models are proposed which are the generalization of the N-gram models in language modeling to extract comprehensive image features.

Abstract

Keyblock, which is a new framework we proposed for content-based 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 the Vector Quantization (VQ) method which has been used for image compression. Then an image can be represented as a code matrix in which the elements are the indices of keyblocks in a codebook. Based on this image representation, information retrieval and database analysis techniques developed in the text domain can be generalized to image retrieval. In this paper, we propose new models named N-block models which are the generalization of the N-gram models in language modeling to extract comprehensive image features. The effort to capture context in a text document motivated the N-gram models. Similarly, the attempt to capture the content in an image motivates us to consider the correlations of keyblocks within an image. By comparing the performance of our approach with conventional techniques using color feature and wavelet texture feature, the experimental results demonstrate the effectiveness of these N-block models.