Artificial Transportation systems (ATS) provide a comprehensive perspective to study actual transportation systems, which are a kind of open and complex giant system referring to diverse engineering and social discipl...
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In order to meet the requirements of stable person detection and tracking techniques in dynamic visual system, we propose a simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorit...
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ISBN:
(纸本)9781467313988
In order to meet the requirements of stable person detection and tracking techniques in dynamic visual system, we propose a simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorithm for person detection and tracking. Based on the simplified minimum enclosing ball (MEB) method, we propose a simplified and fast incremental algorithm to compute the MEB. By utilizing the equivalence between MEB and the dual problem in SVM, we achieve the online and incremental adjustment of the SVM classifier coefficients. The proposed method do not need to solve the quadratic programming problem. It is fast for training. Moreover, it can achieve the online update of classifiers for object tracking with small sample size. Finally, the efficiency of the proposed incremental SVM is validated by detection experiments on dynamic pedestrians tracking system.
The paper discusses two important classification techniques, Fisher's linear discriminated analysis (FLDA) and Support Vector Machine (SVM). First, we propose a theoretical discussion, and then implement FLDA and ...
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Making recognition more reliable under unconstrained environment is one of the most important challenges for realworld face recognition. In this paper, we propose a novel approach for unconstrained face verification. ...
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Making recognition more reliable under unconstrained environment is one of the most important challenges for realworld face recognition. In this paper, we propose a novel approach for unconstrained face verification. First, we use a spectral-clustering method based on Structural Similarity index to estimate the captured environments of facial images. Then for each pair of environments, we learn two coupled metrics, such that facial images captured in different environments can be transformed into a media subspace, and high recognition performance can be achieved. The coupled transformations are jointly determined by solving an optimization problem in the multi-task learning framework. Experimental results on the benchmark dataset (LFW) show the effectiveness of the proposed method in face verification across varying environments.
The rapid increasing popularity of micro-blogging has made it an important information seeking channel. By detecting recent popular topics from micro-blogging, we have opportunities to gain insights into internet hots...
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The rapid increasing popularity of micro-blogging has made it an important information seeking channel. By detecting recent popular topics from micro-blogging, we have opportunities to gain insights into internet hotspots. Generally, a topic's popularity is determined by two primary factors. One is how frequently a topic is discussed by users, and the other is how much influence those users have, since topics shown in the influential users' posts are more likely to attract others' attention. However, existing approaches interpret a topic's popularity with only the number of keywords related to it, which neglect the importance of the user influence to information diffusion in micro-blogging. In this paper, drawing upon the Cognitive Authority Theory and Social Network Theory, we propose a novel model that detects the most popular topics in micro-blogging with a user interest-based method. The proposed model first constructs a topic graph according to users' interests and their following relationship, and then calculates the topics' popularity with a link-based ranking algorithm. The popular topics detected by the method can reflect the relationship among users' interests, and the topics in the posts of influential users can be highlighted. Experimental results on the data of Twitter, a well-known and feature-rich micro-blogging service, show that the proposed method is effective in popular topic discovery.
In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of ...
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In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. ...
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Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this paper, we explicitly consider such attribute-object relationship, and correspondingly, we modify the multi-task learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. Our method is verified on two challenging datasets (KTH and Olympic Sports), and the experimental results demonstrate that our method achieves better results than previous methods in human action recognition.
Cloud classification plays an essential role in a large number of applications. However, this issue is particularly challenging due to the extreme appearance variations under different atmospheric conditions. In this ...
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Cloud classification plays an essential role in a large number of applications. However, this issue is particularly challenging due to the extreme appearance variations under different atmospheric conditions. In this paper, a novel descriptor named illumination-invariant completed local ternary pattern (ICLTP) is proposed for cloud classification. The proposed descriptor effectively handles the illumination variations by introducing illumination invariant factor. Furthermore, the Quadratic-Chi metric, which is more suitable for comparing the difference between two histograms, is applied instead of Chi-Square metric. The experimental results demonstrate the superior performance of our strategy on two challenging cloud databases. Besides cloud classification, we further validate the proposed ICLTP operator on traditional texture classification, which show the good generalization ability.
Public traffic is required for the convenient travel, reducing traffic congestion and accidents, low-carbon, environmental protection, sustainable development, and traffic demand of giant sports and large business act...
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Public traffic is required for the convenient travel, reducing traffic congestion and accidents, low-carbon, environmental protection, sustainable development, and traffic demand of giant sports and large business activities, etc. For public transport demand of the 16th Asian Games and the 2010 Asian Para Games held in Guangzhou in 2010, based on ACP approach, Parallel Public Transport management And control System (PPTMS) for Guangzhou Asian Games had been developed to support management decision of public transport. This system can help public transport managers to improve and enhance significantly the level of public transport management in Guangzhou, from experience-based formulation and manual implementation to scientific computation-based formulation and automatic implementation with intelligent systems, to guarantee the traffic demand effectively during the two Games.
In this paper, by employing an online algorithm based on policy iteration (PI), an adaptive optimal control problem for continuous-time (CT) nonlinear partially uncertain dynamic systems is investigated. In this propo...
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In this paper, by employing an online algorithm based on policy iteration (PI), an adaptive optimal control problem for continuous-time (CT) nonlinear partially uncertain dynamic systems is investigated. In this proposed algorithm, a discounted cost function is discussed, which is considered to be a more general case for optimal control problems. Two neural networks (NNs) are used to implement the algorithm, which aims at approximating the cost function and the control law, respectively. The uniform convergence to the optimal control is proven, and the stability of the system is guaranteed. An illustrating example is given.
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