Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, w...
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ISBN:
(纸本)9781457701221
Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.
This paper proposes a novel budget model based on differential game to deal with budget allocation in competitive search advertisements under a finite time horizon, with consideration of budget constraints. We extend ...
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This paper proposes a novel budget model based on differential game to deal with budget allocation in competitive search advertisements under a finite time horizon, with consideration of budget constraints. We extend the advertising response function with the dynamical advertising effort u and quality score q to fit search advertising scenarios. We also discuss Nash equilibriums of our model, and study some desirable properties of two kinds of equilibriums in the case with budget constraints: "budget-stable" open-loop Nash equilibrium (BS-OLNE) and "budget-unstable" open-loop Nash equilibrium (BUS-OLNE). We have evaluated our budget model and identified properties with computational experiments. Experimental results show that budget strategies with dynamical advertising elasticity are superior to those with fixed one and our findings on OLNEs are helpful for advertisers to make budget decisions.
Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handli...
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ISBN:
(纸本)9781457701221
Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale framework, which combines both texture and intensity feature, to achieve a robust and accurate solution. Our contributions are three folds: first, we provide a multi-scale analysis for the issue;second, for texture feature we propose a novel texture operator named Scale-invariant Centersymmetric Local Ternary Pattern, and a corresponding Pattern Adaptive Kernel Density Estimation technique for its probability estimation;third, we design a Simplified Gaussian Mixture Models for intensity feature. Our method is tested on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. The experimental results clearly demonstrate that our method is superior to the previous methods.
Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes disc...
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ISBN:
(纸本)9781457701221
Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.
The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search f...
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Online advertisers bidding in keyword auctions through Web search engines are experiencing fierce competition. Based on a model of advertisers' rational competitive preference, we propose a novel solution concept ...
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Online advertisers bidding in keyword auctions through Web search engines are experiencing fierce competition. Based on a model of advertisers' rational competitive preference, we propose a novel solution concept called the Upper Bound Nash Equilibrium(UBNE) targeting at modeling the competitive bidding dynamics. UBNE yields the best outcome for search engines, and provides a rational explanation of the bid inflation dynamics on keyword markets.
Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the...
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As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, ...
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In this work, we took the analysis of neural interactions change in M1 of a monkey during the adaptation process for it to complete reach-to-grasp tasks with external perturbation across days. BN model was applied to ...
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Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiven...
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Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiveness is jeopardized by the severe click frauds conducted by advertisers and third-party publishers and the vicious bidding strategy used by advertisers to exhaust the budget of rivals. In this paper, we analyze the drawbacks of GSP that tolerate or even encourage such negative behaviors (i.e., click fraud and vicious bidding) and propose a dynamic modification of the original GSP mechanism to address these drawbacks. Our modified auction mechanism incorporates budget into slot allocation and payment determination and relates the quality score of an advertisement to the current bid. Our analysis shows that our mechanism can effectively reduce the effects of click fraud and vicious bidding.
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