  Incompatibility of image descriptor and ranking is always neglected in image
retrieval. In this paper, manifold learning and Gestalt psychology theory are
involved to solve the incompatibility problem. A new holistic descriptor called
Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed,
which combines color and gradient direction to imitate the human visual
uniformity. PUD features in the same class images distributes on one manifold
in most cases because PUD improves the visual uniformity of the traditional
descriptors. Thus, we use manifold ranking and PUD to realize image retrieval.
Experiments were carried out on five benchmark data sets, and the proposed
method can greatly improve the accuracy of image retrieval. Our experimental
results in the Ukbench and Corel-1K datasets demonstrated that N-S score
reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by
utilizing PUD which has only 280 dimension. The results are higher than other
holistic image descriptors (even some local ones) and state-of-the-arts
retrieval methods.
