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This paper compares fixed partitioning and salient points schemes for dividing an image into patches, in combination with low-level MPEG-7 visual descriptors to represent the patches with particular patterns. A clustering technique is applied to construct a compact representation by grouping similar patterns into a cluster codebook. The codebook will then be used to encode the patterns into visual keywords. In order to obtain high-level information about the relational context of an image, a correlogram is constructed from the spatial relations between visual keyword indices in an image. For classifying images a k-nearest neighbors (k-NN) and a support vector machine (SVM) algorithm are used and compared. The techniques are compared to other methods on two well-known datasets, namely Corel and PASCAL. To measure the performance of the proposed algorithms, average precision, a confusion matrix, and …