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This paper presents a novel approach to represent the codebook vocabulary in bag-of-features model for scene categorization. Traditional bag-of-features model describes an image as a histogram of the occurrence rate of codebook vocabulary. In our approach, spatial correlogram between codewords is incorporated to approximate the local geometric information. This works by augmenting the traditional vocabulary histogram with the distance distribution of pairwise interest regions. We also combine this correlogram representation with spatial pyramid matching to describe both local and global geometric correspondences. Experimental results show that correlogram representation can outperform the histogram scheme for bag-of-features model, and the combination with spatial pyramid matching improves effectiveness for categorization.