VLAD Encoding Based on LLC for Image Classification

Abstract

The Vector of Locally Aggregated Descriptors (VLAD) method, developed from BOW and Fisher Vector, has got great successes in image classification and retrieval. However, the traditional VLAD only assigns local descriptors to the closest visual words in the codebook, which is a hard voting process that leads to a large quantization error. In this paper, we propose an approach to fuse VLAD and locality-constrained linear coding (LLC), compared with the original method, several nearest neighbor centers are considered when assigning local descriptors. We use the reconstruction coefficients of LLC to obtain the weights of several nearest neighbor centers. Due to the excellent representation ability of the reconstruction coefficients for local descriptors, we also combine it with VLAD coding. Experiments were conducted on the 15 Scenes, UIUC Sports Event and Corel 10 datasets to demonstrate that our proposed method has outstanding performance in terms of classification accuracy. Our approach also does not generate much additional computational cost while encoding features.