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This paper presents a new class of 2D string kernels, called spatial mismatch kernels, for use with support vector machine (SVM) in a discriminative approach to the image categorization problem. We first represent images as 2D sequences of those visual keywords obtained by clustering all the blocks that we divide
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images
task where the goal is to reject outliers from a set of images returned for a keyword query. Furthermore, it is evaluated on the supervised classification tasks with the challenging VOC2005 data set. Our approach yields excellent accuracy in the unsupervised ranking task compared to a recently proposed probabilistic model
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