In this paper a novel approach for dissimilarity-based representation is presented, which combines local image descriptors with several dissimilarity functions. The basic idea consists of defining the set of prototypes in terms of local descriptors of image parts, namely feature points extracted from the training set. Therefore, according to the dissimilarity-based approach, a new image can be characterized on the basis of its dissimilarity with each of the given prototypes. This leads to a new class of Local Kernels which exploits the use of dissimilarities between image parts. In particular, we show that the classic Bag-of-Feature (BoF) kernel can be revised as a special case of our new formulation, and better performance can be obtained when new dissimilarity functions are employed. Moreover, we observe that any variants of the basic BoF kernel can take advantage from our approach as we show for the case of the Pyramid Match kernel. Promising results are shown for image categorization on the ETH-80 database.