Target classification using SIFT sequence scale invariants

摘要

On the basis of scale invariant feature transform(SIFT) descriptors,a novel kind of local invariants based on SIFT sequence scale(SIFT-SS) is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine(SVM) with radial based function(RBF) kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants(AMI) and multi-scale auto-convolution(MSA) in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.