SIFT-based Elastic sparse coding for image retrieval

Bag-of-features (BoF) model based on SIFT generally assumes each descriptor is related to only one visual word of the codebook. Therefore, the potential correlation between the descriptor and other visual words is ignored. On the other hand, sparse coding through l₁-norm regularization fails to generate optimal sparse representations since l₁-norm regularization randomly selected one variable from a group of highly correlated variables. In this study we propose a novel bag-of-features model for image retrieval called SIFT-based Elastic sparse coding. The method utilizes a large number of SIFT descriptors to construct the codebook. The Elastic Net regression framework, which combines both l₁-norm and l₂-norm penalties, is then used to obtain the sparse-coefficient vector corresponding to the SIFT descriptor. Finally each image can be represented by a unified sparse-coefficient vector. Experimental results on Coil20 dataset demonstrate the consistent superiority of the proposed method over the state-of-the-art algorithms including original SIFT matching, conventional BoF strategy and BoF model based on l₁-norm sparse coding.