基于随机旋转局部保持哈希的图像检索技术

针对基于局部保持投影（locality preserving projection，LPP）的哈希用于图像检索造成图像表征力不强、检索效率低下的问题，融合LPP及主成分分析（principal component analysis，PCA）技术，提出一种随机旋转局部保持哈希的图像检索算法。首先对样本进行PCA降维，对PCA变换矩阵进行随机旋转形成PCA降维矩阵，将原始样本在降维矩阵上进行投影，得到PCA降维样本。为充分利用样本间的相似性结构，对PCA降维样本进行LPP映射，并引入随机矩阵对特征向量进行偏移构造最终编码投影矩阵。再将原始样本投影到编码投影矩阵，得到最终的降维样本；最后对其进行哈希编码，得到有效的二进制编码用于图像检索。算法充分考虑样本间的全局和局部相似性结构，体现了样本间所蕴含的局部和全局信息，把随机旋转应用于PCA降维矩阵，减少了编码之间的量化误差，提高了图像特征的识别能力。分别在3个人脸数据集上进行性能测试实验，并与相关方法进行比较，得到了较好的效果。实验结果表明该方法是有效的。

Image Retrieval Based on Random Rotation Locality Preserving Hashing

In order to solve the problem that locality preserving projection hashing can results in the poor expression of image feature and lower retrieval efficiency when it is applied in image retrieval, a novel image retrieval method based on hashing combining principal component analysis (PCA) with locality preserving projection (LPP) is proposed. Firstly, the sample is reduced dimension with PCA, a random matrix is introduced to make rotation of the PCA transformational matrix. The original sample is projected into the PCA transformational matrix and the reduced-dimension PCA sample is achieved. Meanwhile, the similarity structure between samples is taken into account. The reduced-sample is mapped with LPP. On these basis, the projection matrix is constructed with a random matrix. Finally, the original sample is projected into the projection matrix and the hash coding is achieved. The presented method can keep the local and overall similarity structure of the samples because of the application of PCA and LPP. Furthermore, the quantization error between codes is reduced by introducing of random rotation, thus improving the efficiency of image retrieval. Experiments show that the proposed method can achieve better performance compared with other traditional methods.