Comparative Assessment of Content-Based Face Image Retrieval in Different Color Spaces
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 *, I 1 I 2 I 3, 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.
KeywordsColor Space Retrieval Performance Kernel Principal Component Analysis Chromaticity Diagram Cosine Similarity Measure
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