We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face imag...
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ISBN:
(纸本)9781424404759
We propose two geometric structure based approaches GGCI (global geometric clustering for image) and GSIM (geometric structure based image matching) for image clustering and image matching, respectively. For face images or object images taken with varying factors, the GGCI approach learns the global geometric structure of images space and clusters images based on geodesic distance instead of Euclidean distance and the extended nearest neighbor approach. The GSIM approach uses the minimal Euclidean distance between parts of image and the pattern and its variations as matching criteria and threshold strategy for image matching. We demonstrate experimentally that the GGCI approach achieves lower error rates and the GSIM approach brings down the sensitivity of gray values to change in radiometry and reduces multi local extrema to some extent.
Large-field high-resolution electron tomography enables visualizing detailed mechanisms under global structure. As field enlarges, the processing time increases and the distortions in reconstruction become more critic...
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Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity *** address these problems,on the one hand,some CF met...
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Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity *** address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender ***,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the ***,they maybe lack of calibrated probabilistic predictions and make overly confident *** this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for ***,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent *** that,the outputs of these two neural networks are combined to produce the final ***,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic *** experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.
Curvelet transform is the combination of the multi-scale analysis and multi-directional analysis transforms, which is more suitable for objects with curves. Applications of the curvelet transform have increased rapidl...
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We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-enhanced RNN decoder is called MEMDEC....
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Thinning algorithms can be classified into two general types: serial and parallel algorithms. Several algorithms have been proposed, but they have limitations. A new thinning algorithm based on the centroid of the blo...
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With tremendous research progress in biomedical sensors and sensor networks, there is an increasingly need for employing new data processing technologies that are capable of online analysis of the streaming medical se...
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Emotion study is a multi-disciplinary research subject. In the past three decades, a number of theoretical models of emotion and computer applications have been proposed from different perspectives including psycholog...
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Block transform compressed videos usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. The inter prediction utilized in video coding also induces block ...
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Recently, cascade instance segmentation inspired by cascade object detection has achieved notable performance. Due to the lack of global information, many methods suffer from incomplete segmentation such as missing ed...
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