Fixed partitioning and salient points with MPEG-7 cluster correlograms for image categorization
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 ROC-curves are used. The results show that the cluster correlogram outperforms the cluster histogram. The saliency based scheme performs similarly to the fixed partitioning scheme and the SVM significantly outperforms the k-NN classifier. Finally, we demonstrate the robustness to noise, photometric, and geometric distortions.
cluster correlogram computer vision image indexing and retrieval