Dual Adaptive Representation of Vector of Locally Aggregated
This paper addresses the problem of large-scale image retrieval.We use the dual adaptive representation of vector of locally aggregated to improve the retrieval efficiency.The vector of locally aggregated (VLAD) aggregates SIFT descriptors and produces a compact representation to improve the search accuracy and memory usage,and the usage of adapted cluster centers of the VLAD enhances the performance further.We first carry out twice adaptation on the cluster centers to optimize the references of the features which are used to calculate the center residuals,and to obtain the vector of an image by jointing the center residuals of each corresponding cluster in the initial retrieval process.We then reduce the dimensionality of the vectors by using PCA,and evaluate the similarities between query image the top N result image by the residual of sparse representation in the re-rank process.Finally,experiments show clearly that our work improves the retrieval accuracy.
Image retrieval Image search Sparse representation Adaptive representation
Hui Lv Tao Lei Xianglin Huang Yakun Zhang
Tian Yun Rongchuang Data Technology Co. Ltd.Beijing, China Communication University of China Beijing, China Tianjin Branch of China Xian Satellite Control Center Tianjin,China
国际会议
重庆
英文
686-689
2015-12-19（万方平台首次上网日期，不代表论文的发表时间）