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—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(More)
Single feature is inefficient to describe content of an image, which is a shortcoming in traditional image retrieval task. We know that one image can be described by different features. Multi-feature fusion ranking can be utilized to improve the ranking list of query. In this paper, we first analyze graph structure and multi-feature fusion re-ranking from(More)
Displaying large-scale 3D vector data in landscape map is very important, as 3D vector data can provide many important information, such as: precise geographic boundaries, areas, 3D text marks, different attributes identity, precise path and many important invisible information in real world (e.g.: underground things) and etc. In this paper, we present a(More)
—Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognition. Our feature fusion utilizes RGB and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and utilize word(More)
Convolutional Neural Network(CNN) is a kind of deep learning and it has become a current hot topic in the field of image recognition. In the CNN, Output layer consists of Euclidean Radial Basis Function, unit matrix column as CNN's label vector. The category of the input image can be interpreted as the nearest label vector. This paper addresses a question:(More)
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