Improved cluster center adaption for image classification

The feature coding algorithm, “Vector of Locally Aggregated Descriptors (VLAD)”, can be used effectively for large scale object instance retrieval. Despite its effectiveness and excellent performance, the existence of ambiguous cluster centers can reduce the performance. Though an idea to this problem has been proposed, it is not practical in fact. In this paper, we analyze possible situations that cause effect on the results and propose a novel approach to improve the VLAD method. The proposed method mainly focuses on the similarity measure between each two images. For each two images, we adapt the original cluster center to VLAD vectors. As we illustrate, our method has promising results with small vocabulary size on both datasets of 15 Scenes and VOC2007.