Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicit...
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It's very meaningful to conduct the driver to the parking space available in the parking lot clearly and accurately by computer vision and computational intelligence. While it is an extremely difficult task, becau...
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Rich information is contributed to blogs by millions of users all around the world with the development of blogsphere. However, few work has been done on the study of blog extraction so far. Unlike the traditional tem...
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Rich information is contributed to blogs by millions of users all around the world with the development of blogsphere. However, few work has been done on the study of blog extraction so far. Unlike the traditional template-dependent wrapper, not only blog articles but also blogroll is extracted with template-independent wrapper in this paper. In our method, blog extraction is formalized as a machine learning problem and a template-independent wrapper is learned by using labeled blog pages from a single site. Testing pages are obtained from 10 popular Chinese blog sites. And experimental results on 300 real blog pages indicate that the proposed method can correctly extract data from blogs with the accuracy of 90% or even above.
In course of generating templates of handwritten Chinese characters, the method based on average of the samples features is popular. In this paper, an approach based on quantile is put forward to generate templates. A...
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In course of generating templates of handwritten Chinese characters, the method based on average of the samples features is popular. In this paper, an approach based on quantile is put forward to generate templates. According to our experiments, this method of template generating is robust than that one based on average and it improves the performance of recognitionsystem in the case of not increasing any complexity of the algorithm. At the same time, the method is universal and can be in combination with diversified feature extraction methods.
This paper describes a model for performing action classification in real-time video streaming. This model can simultaneously analyze the spatio-temporal information of video under the constraint of low delay. In addi...
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This paper describes a model for performing action classification in real-time video streaming. This model can simultaneously analyze the spatio-temporal information of video under the constraint of low delay. In addition, in order to prevent the model from judging motionless segments in the video as motion, the model in this article is equipped with the ability of distinguish the segments of motion from the stationary ones. The experimental results show that the model can complete the action classification task with little delay, which ensures that the classification result can be output in real time with the constant input of the video image.
Keypoint detection is important for object recognition, image retrieval, mosaicing etc., and has attracted ample research. In this paper, we propose a novel wavelet-based detector (NWBD) based on the previous research...
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Keypoint detection is important for object recognition, image retrieval, mosaicing etc., and has attracted ample research. In this paper, we propose a novel wavelet-based detector (NWBD) based on the previous researches on keypoint detection. NWBD is performed in wavelet pyramid space, it extracts the local extrema of the energy map computed by intra-scale coefficient product (ISCP) as the candidate keypoint, and then discards some points by Hessian matrix. In the experiments, the novel detector was compared with Harris detector and SIFT detector by the evaluation of repeatability, and it achieved better performance for some scenes in the database provided by Mikolajcyzk and Schmid, such as wall, trees, and graffiti.
Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level ...
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This paper analyses the kernel of the General Regression Neural Network (GRNN) model in detail, and presents its deficiencies in the domain of complex systems forecasting. We import various aspects of the Broyden-Flet...
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ISBN:
(纸本)9780889867420
This paper analyses the kernel of the General Regression Neural Network (GRNN) model in detail, and presents its deficiencies in the domain of complex systems forecasting. We import various aspects of the Broyden-Fletcher-Goldfarb- Shanno (BFGS) Quasi-Newton method and GM(1,h) algorithms to improve the kernel of the GRNN model. We then apply this modified model to the problem of unemployment forecasting in China, as an example of its ability to model time-varying environments.
Cross-entropy loss function (CEL) is widely used for training a multi-class classification deep convolutional neural network (DCNN). While CEL has been successfully implemented in image classification tasks, it only f...
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With the rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty...
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