Comparative assessment of content-based face image retrieval in different color spaces
Comparative assessment of content-based face image retrieval in different color spaces
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DOI：
10.1142/s0218001405004381
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发表时间：
2005-07
期刊：
影响因子：
--
通讯作者：
P. Shih;Chengjun Liu
中科院分区：
文献类型：
--
作者：
P. Shih;Chengjun Liu
基于内容的人脸图像检索是指根据人脸图像的几何特征或统计特征对人脸图像进行计算机检索。众所周知，颜色空间通过颜色不变量、颜色直方图、颜色纹理等为图像索引和检索提供了强大的信息。本文比较了基于内容的人脸图像检索在不同颜色空间中的性能，并使用了目前人脸识别界流行的标准算法--主成分分析算法。特别是，我们通过评估每个颜色空间的7个颜色配置来比较评估12个颜色空间(RGB、HSV、YUV、YCbCR、XYZ、YIQ、L*a*b*、U*V*W*、L*u*v*、I1I2I3、HSI和RGB)。颜色配置由单个颜色分量图像或颜色分量图像的组合定义。以RGB颜色空间为例，可能的颜色配置为R、G、B、RG、RB、GB和RGB。使用对应于200个受试者的1800幅FERET R、G、B图像的实验结果表明，某些颜色配置，如RGB颜色空间中的R和HSV颜色空间中的V，有助于提高人脸检索性能。
Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc.. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating 7 color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB, and RGB. Experimental results using 1,800 FERET R, G, B images corresponding to 200 subjects show that some color configurations, such as R in the RGB color space and V in the HSV color space, help improve face retrieval performance.