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Nowadays, the Bag-of-words (BoW) representation is well applied to recent state-of-the-art image retrieval works. However, with the rapid growth in the number of images, the dimension of the dictionary increases substantially which leads to great storage and CPU cost. Besides, the local features do not convey any semantic information which is very important in image retrieval. In this paper, we propose...
The large amount of SIFT descriptors in an image and the high dimensionality of SIFT descriptor has made problems for large-scale image dataset in terms of speed and scalability. In this paper, we propose a descriptor selection algorithm via dictionary learning and only a small set of features are reserved, which we refer to as TOP-SIFT. We discover the inner relativity between the problem of descriptor...
With the development of computer techniques, 3D model has been used more and more widely and content-based 3D model retrieval has been a hotspot in the area of multimedia information retrieval. How to extract 3D models' feature effectively is still a difficulty. Projection based 3D model feature extraction is an important kind in feature extraction, because of its robustness against noise, simplification...
Curvature on the surface of 3D mesh model is an important discrete differential geometrical descriptor. It can show the curving degree very well for those models with curving pieces and the ones with extreme points or extension components. In this paper, we use mean curvature and corresponding coordinates of the vertexes on the surface as the feature descriptor of model. The descriptor we defined...
How to retrieve more and more 3D models is the important point in pervasive computing. In this paper, a novel ensembling neural network (NN) - based 3D model retrieval method is proposed. Firstly, four NNs are trained by constructed learning algorithm (CLA). These four NNs are trained by using difference feature of models. And then NN assembles are employed to retrieve 3D models. The experiments show...
Relevance feedback schmes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). SVM-based relevance feedback has often bad performance when the number of labeled positive feedback samples is small. This paper presents a method to use the unlabeled data to improve the performance of SVM classifier, which has only a few labeled training examples. We adapt...
This paper proposes a novel methodology based on V-system polynomials for content-based search and retrieval of 3D objects. The V-system is composed by piecewise polynomials, and is capable of exactly describing the geometric information expressed by the popularly and widely used spline curves and surface. We define rotation invariant moments-v-system moment by combining V-system polynomials with...
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