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 seven 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 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.
Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces
