局部敏感哈希图像检索参数优化方法

随着大数据时代的到来,如何及时准确地处理海量的图像、视频等多媒体数据已成为相关领域新的挑战。局部敏感哈希算法在处理高维图像特征数据时表现出了良好的性能,使其成为了近年来的研究热点。针对图像检索算法参数的优化选择问题,提出了一种局部敏感哈希图像检索参数优化方法。首先建立面向图像检索的局部敏感哈希算法的性能优化模型,给出其参数优化所对应的非线性最优化问题的一般形式,并且定义了新的优化目标函数;然后分析图像数据间的距离分布规律,发现了求解上述参数优化问题的快速方法;最后结合数值微分和二分查找提出相应的局部敏感哈希参数优化算法。实验结果表明,该方法可以大幅降低算法的复杂度,提高运行效率,同时保持较高的精确值和召回率的调和均值F_1。

Parameter Optimization Method for Locality Sensitive Hash Image Retrieval

With the advent of the era of big data,how to process massive images,videos and other multimedia data in a timely and accurate manner has become a new challenge in related fields.Due to its great performance in processing high-dimensional image feature data,locality sensitive hash(LSH)algorithm has become a research hotspot in recent years.In order to optimize the parameters of image retrieval algorithm,we propose a parameter optimization method for LSH image retrieval.Firstly,a performance optimization model of LSH for image retrieval is established,the general form of the non-linear optimization problem for LSH parameter optimization is given,and the novel optimized objective function is defined.Moreover,the distance distribution between image data is analyzed,and a quick method for solving the parameter optimization problem aforementioned is found.Finally,a parameter optimization algorithm for LSH is proposed based on numerical differentiation and binary search.The experiment shows that the proposed method can greatly reduce the complexity and improve the efficiency of the algorithm,while maintaining a high harmonic mean F1 of precision and recall.