摘要
针对画像块和照片块在流形上的邻域关系并不能完全反映彼此内在数据结构的问题,提出一种基于局部约束邻域嵌入(LCNE)的画像-照片合成算法。首先,利用基于邻域嵌入(NE)的合成方法得到待合成照片或画像的初始估计;其次,根据待合成的照片块或画像块与训练集中的照片块或画像块的相似性来约束合成权值;然后,通过交替优化方法进行权值的确定和K-近邻的选择,并更新待合成目标块;最后,合并所有估计的照片块或画像块合成目标图像。与基于邻域嵌入的画像照片合成方法相比,所提方法合成图像的结构相似度提高0.050 3,脸识别准确率提高14%。实验结果表明,该方法解决了基于NE方法导致的邻域之间兼容性不强的问题,能大大减少合成图像上的噪声及变形。
The neighboring relationship of sketch patches and photo patches on the manifold cannot always reflect their intrinsic data structure. To resolve this problem, a Locality-Constrained Neighbor Embedding （LCNE） based face sketch-photo synthesis algorithm was proposed. The Neighbor Embedding （NE） based synthesis method was first applied to estimate initial sketches or photos. Then, the weight coefficients were constrained according to the similarity between the estimated sketch patches or photo patches and the training sketch patches or training photo patches. Subsequently, alternative optimization was deployed to determine the weight coefficients, select K candidate image patches and update the target synthesis patch. Finally, the synthesized image was generated by merging all the estimated sketch patches or photo patches. In the contrast experiments, the proposed method outperformed the NE based synthesis method by 0.0503 in terms of Structural SIMilarity （SSIM） index and by 14% in terms of face recognition accuracy. The experimental results illustrate that the proposed method resolves the problem of weak compatibility among neighbor patches in the NE based method and greatly alleviates the noises and deformations in the synthetic image.
出处
《计算机应用》
CSCD
北大核心
2015年第2期535-539,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(81160182
61201125)
关键词
人脸画像-照片合成
局部约束
邻域嵌入
流形学习
人脸识别
face sketch-photo synthesis
local constraint
Neighbor Embedding （NE）
manifold learning
facerecognition
