  Local feature descriptors have been widely used in fine-grained visual object
search thanks to their robustness in scale and rotation variation and cluttered
background. However, the performance of such descriptors drops under severe
illumination changes. In this paper, we proposed a Discriminative and Contrast
Invertible (DCI) local feature descriptor. In order to increase the
discriminative ability of the descriptor under illumination changes, a Laplace
gradient based histogram is proposed. A robust contrast flipping estimate is
proposed based on the divergence of a local region. Experiments on fine-grained
object recognition and retrieval applications demonstrate the superior
performance of DCI descriptor to others.
