一种运用随机算法改进的图像检索方法

An improved image retrieval method using random algorithms

传统基于局部特征表示的图像检索方法在图像特征提取和特征相似性匹配时计算量较大,为此提出一种运用随机算法进行改进的图像检索方法。在图像特征提取方面,通过随机采样获得数量适当的像素点作为特征点,用SIFT(scale invariant feature transform)算子对随机特征点进行描述以形成图像的有效表示;在特征相似性匹配方面,采用基于随机映射的LSH(locality sensitive hashing)算法为图像特征库建立索引,并用于对所查询图像的局部特征进行高效的近似近邻搜索。实验结果表明,该方法有效降低了图像检索的计算复杂度,提高了检索效率。

An image retrieval method using random algorithms is proposed to improve the traditional local feature representation method which often needs a large amount of calculation during image feature extraction and similarity matching.For image feature extracting,the method adopts random sampling to obtain an appropriate number of image pixels as the feature points,then represents these random feature points with SIFT descriptors in order to form an effective image representation.For feature similarity matching,it applies a random mapping LSH algorithm to indexing the feature database and conducting the efficient approximate nearest neighbor query of image local features.Experimental results show that the proposed method can efficiently reduce the computation complexity and improve the image retrieval efficiency.