Scene Image Classification Based on Improved VLAD Reprensentation

Abstract

Vector of Locally Aggregated Descriptors (VLAD) method, which aggregates descriptors and produces a compact image representation, has achieved great success in the field of image classification and retrieval. However, the original VLAD method is a hard assignment strategy that only assigns each descriptor to the nearest neighbor visual word in dictionary, which leads to large quantization error. In this paper, improved VLAD based on adaptive bases and saliency weights is proposed to solve the above problem. The new method considers the local density distribution when assigning local descriptors, adaptively selects several nearest neighbor visual words, and takes the coding coefficients obtained by utilizing saliency as the weights of the selected visual words. Experimental results on Corel 10, 15 Scenes and UIUC Sports Event datasets show that the new coding method proposed in this paper achieves better classification performance compared with the existing five VLAD based methods and two commonly used representation methods.