Speed Up Spatial Pyramid Matching Using Sparse Coding with Affinity Propagation Algorithm

Abstract

AbstractRecently support vector machines (SVMs) combining spatial pyramid matching (SPM) kernel have been highly successful in image annotation. And linear spatial pyramid matching using sparse coding (ScSPM) scheme was proposed to enhance the performance of SPM both in time and annotation accuracy. However, both of these algorithms suffer from expansibility problem, and ScSPM needs quite a long time for codebook construction. In this paper, we proposed an adjusted framework for the ScSPM algorithm, which applies multi-level affinity propagation (AP) algorithm to the codebook construction process (AP-ScSPM). This novel approach can remarkably reduces the time complexity of codebook construction process. Furthermore, as AP algorithm can automatically determine the representative vector number, the expansibility of the algorithm is improved. By a series of experiments, we find that the proposed framework greatly reduces the time of codebook construction process and has the same performance in terms of annotation accuracy with ScSPM.KeywordsImage annotationAffinity propagationSpatial pyramid matchingScale invariant feature transform