A new class of kernels for object recognition based on local image feature representations are introduced in this paper. Formal proofs are given to show that these kernels satisfy the Mercer condition. In addition, multiple types of local features and semilocal constraints are incorporated. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL-100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations.