Target classification using SIFT sequence scale invariants

Abstract

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.