Product quantization is now considered as an effective approach to solve the approximate nearest neighbor(ANN)search.A collection of derivative algorithms have been developed.However,the current techniques ignore the ...Product quantization is now considered as an effective approach to solve the approximate nearest neighbor(ANN)search.A collection of derivative algorithms have been developed.However,the current techniques ignore the intrinsic high order structures of data,which usually contain helpful information for improving the computational precision.In this paper,aiming at the complex structure of high order data,we design an optimized technique,called optimized high order product quantization(O-HOPQ)for ANN search.In O-HOPQ,we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way.As a result,spatial adjacent elements in the high order data space are grouped into the same subspace.Then,O-HOPQ generates its spatial structured codebook,by optimizing the quantization distortion.Starting from the structured codebook,the global optimum quantizers can be obtained effectively and efficiently.Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers.Besides,the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.展开更多
海量图像检索算法的核心问题是如何对特征进行有效的编码以及快速的检索.局部集聚向量描述(Vector of locally aggregated descriptors,VLAD)算法因其精确的编码方式以及较低的特征维度,取得了良好的检索性能.然而VLAD算法在编码过程中...海量图像检索算法的核心问题是如何对特征进行有效的编码以及快速的检索.局部集聚向量描述(Vector of locally aggregated descriptors,VLAD)算法因其精确的编码方式以及较低的特征维度,取得了良好的检索性能.然而VLAD算法在编码过程中并没有考虑到局部特征的角度信息,VLAD编码向量维度依然较高,无法支持实时的海量图像检索.本文提出一种在VLAD编码框架中融合重力信息的角度编码方法以及适用于海量图像的角度乘积量化快速检索方法.在特征编码阶段,利用前端移动设备采集的重力信息实现融合特征角度的特征编码方法.在最近邻检索阶段将角度分区与乘积量化子分区相结合,采用改进的角度乘积量化进行快速近似最近邻检索.另外本文提出的基于角度编码的图像检索算法可适用于主流的词袋模型及其变种算法等框架.在GPS及重力信息标注的北京地标建筑(Beijing landmark)数据库、Holidays数据库以及SUN397数据库中进行测试,实验结果表明本文算法能够充分利用匹配特征在描述符以及几何空间的相似性,相比传统的VLAD以及协变局部集聚向量描述符(Covariant vector of locally aggregated descriptors,CVLAD)算法精度有明显提升.展开更多
基金the National Natural Science Foundation of China(Grant No.61732011)Applied Fundamental Research Program of Qinghai Province(2019-ZJ-7017).
文摘Product quantization is now considered as an effective approach to solve the approximate nearest neighbor(ANN)search.A collection of derivative algorithms have been developed.However,the current techniques ignore the intrinsic high order structures of data,which usually contain helpful information for improving the computational precision.In this paper,aiming at the complex structure of high order data,we design an optimized technique,called optimized high order product quantization(O-HOPQ)for ANN search.In O-HOPQ,we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way.As a result,spatial adjacent elements in the high order data space are grouped into the same subspace.Then,O-HOPQ generates its spatial structured codebook,by optimizing the quantization distortion.Starting from the structured codebook,the global optimum quantizers can be obtained effectively and efficiently.Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers.Besides,the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.
基金Anhui Provincial Natural Science Foundation(1308085QA18)Key Project of Anhui Province (11070203010)Natural Science Project of the Education Department of Anhui Province(2012SQRL209,KJ2012Z274, 2011SQRL159)
