Different Orderings and Visual Sequence Alignment Algorithms for Image Classification

Pawel Drozda, Krzysztof Sopyla, Przemyslaw Górecki

Abstract: This paper presents a successful connection of different sequence alignment algorithms with Bag of Visual Words concept for image classification. In particular, sequences were created on the basis of dense SIFT descriptors, for which different types of sequence orderings were proposed. Then, the similarities between images were calculated with two different sequence alignment algorithms. Finally, the SVM algorithm was proposed as a classifier. The obtained results showed that both sequence alignment algorithms obtain very similar results and that the type of ordering affects the accuracy very slightly.