基于QBFM矩和三维结构的图像哈希算法

Image hashing algorithm based on QBFM moments and three-dimensional structure

为了增强图像哈希算法的分类性能并提高拷贝检测的准确率和效率,提出基于QBFM矩和三维结构的图像哈希算法。首先对彩色图像进行规格化处理,并通过多尺度融合得到高斯融合图像和拉普拉斯融合图像,再对两种融合图像分别提取QBFM特征。同时直接在RGB颜色空间提取高斯融合图像的梯度图像并构造三维模型,利用不同视角下梯度峰顶和峰谷曲线的凹凸点信息得到三维局部结构特征;再对三维模型等距切分,统计各切面的像素数和方差作为三维全局结构特征。最后,将图像的QBFM特征和三维特征结合并置乱构成最终的哈希序列。实验结果表明,算法在鲁棒性和区分性之间有更好的平衡;与现有的哈希算法相比具有较好的图像分类性能;在拷贝检测实验中,算法具有最优的查全率和查准率。

To enhance the performance of image classification and improve the accuracy and efficiency of copy detection, this paper proposed an image hash algorithm based on QBFM moments and three-dimensional structure.Firstly, it used normalization to process the color image, and obtained the Gaussian fusion image and Laplace fusion image through multi-scale fusion, then extracted the QBFM features of two fusion images.At the same time, it directly extracted gradient information of the Gaus-sian fusion image in the RGB color space and constructed a three-dimensional model, used the concave convex point information of peak and valley curve of gradient from different perspectives to obtain the three-dimensional local structure features.Then, it disposed the three-dimensional model of gradient image by equidistant segmentation, counted the number of pixels and variance of each section as the three-dimensional global structure features.Finally, it combined the QBFM features and three-dimensional features of the image and scrambled to form the final hash sequence.Experimental results show that the algorithm has a better balance between robustness and discrimination.Compared with the existing hash algorithms, it has good image classification performance.In the copy detection experiment, the algorithm has the best recall and precision.