ColorPCA: Color Principal Feature Extraction Technique for Color Image Reconstruction and Recognition

This paper introduces an effective mechanism to extract informative principal features from the color images and proposes a color principal feature extraction technique referred to as ColorPCA. ColorPCA performs in the color image space, extracting the principal features directly from the color images. As a result, the color and local topological information of pixels at each level of the color images can be effectively preserved. In extracting the most representative features, a color image scatter matrix is constructed and its eigenvectors are employed for color principal feature extraction. ColorPCA has only one parameter (i.e., the reduced dimension) to estimate and the projection axes can be effectively obtained using eigen-decomposition. Extensive color image reconstruction and recognition over the benchmark problems verified the effectiveness of the presented ColorPCA. Results show that ColorPCA can effectively reconstruct the color images. Image recognition also demonstrates that ColorPCA can deliver promising results compared with other state-of-the-art 1D and 2D principal feature extraction algorithms.