Color independent components based SIFT descriptors for object/scene classification

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-STFT) for object/scene classification. We first learn an efficient color trans format ion matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-hased color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-STFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can hoost the ohjects and suppress the background, the proposed CIC-SIFT can extract mure elective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.