7567561
Genetic Algorithm based Selective Neural Network Ensemble
Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named GASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.
2718321
Face recognition with one training image per person
At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)2A, is proposed. (PC)2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.
1886465
Learning a Rare Event Detection Cascade by Direct Feature Selection
Face detection is a canonical example of a rare event detection problem, in which target patterns occur with much lower frequency than non-targets. Out of millions of face-sized windows in an input image, for example, only a few will typically contain a face. Viola and Jones recently proposed a cascade architecture for face detection which successfully addresses the rare event nature of the task. A central part of their method is a feature selection algorithm based on AdaBoost. We present a novel cascade learning algorithm based on forward feature selection which is two orders of magnitude faster than the Viola-Jones approach and yields classifiers of equivalent quality. This faster method could be used for more demanding classification tasks, such as on-line learning.
15943479
Efficient face candidates selector for face detection
Abstract In this paper an efficient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by finding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.
412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.
8437622
A Greedy Algorithm for Capacity-Constrained Surrogate Placement in CDNs
One major factor that heavily affects the performance of a content distribution network (CDN) is placement of the surrogates. Previous works take a network-centric approach and consider only the network traffic. In this paper, we propose solutions to optimal surrogate placement, taking into consideration both network latency and capacity constraints on the surrogates. For CDNs with a tree topology, an efficient and effective greedy algorithm is proposed which minimizes network traffic while at the same time maximizing system throughput. Simulation results show that the greedy algorithm is far better than the existing optimal placement scheme that makes decisions based solely on network traffic. This suggests that capacity constraints on surrogates or server bottlenecks should be considered when determining surrogate placement, especially when the capacities of CDN servers are limited.
6952885
On Finding a Hamiltonian Path in a Tournament Using Semi-Heap
The problem of sorting an intransitive total ordered set, a generalization of regular sorting, is considered. This generalized sorting is based on the fact that there exists a special linear ordering (also called a generalized sorted sequence) for any intransitive total ordered set, or equivalently, the existence of a Hamiltonian path in a tournament. A new data structure called semi-heap is proposed to construct an optimal Θ(n log n) sorting algorithm. We also provide a cost-optimal parallel algorithm using semi-heap. The run time of this algorithm is Θ(n) with Θ(log n) processors under the EREW PRAM model. The use of a Hamiltonian path (generalized sorting sequence) as an approximation of a ranking system in a tournament is also discussed.
5816274
Available bandwidth detection with improved transport control algorithm for heterogeneous networks
In this paper, we propose a transport control performance improvement algorithm in heterogeneous networks. This paper discusses some novel algorithms to estimate the available bandwidth and to distinguish congestion error losses from wireless transmission error losses. The main characteristics of the algorithms is that it is nonintrusive, i.e., it does not need to inject the traffic into the network in order to estimate the available bandwidth. Thus, our algorithms proposed can improve the throughput and can also be applied to different types of networks adaptively. We report experimental results to efficiency of the proposed algorithm.
8935539
An extended dynamic source routing scheme in ad hoc wireless networks
We consider a multipath extension to the dynamic source routing (DSR) protocol proposed by D.B. Johnson and D.A. Maltz (1996), an on-demand routing protocol, for ad hoc wireless networks. This extension keeps two node disjoint paths between the source and the destination of a routing process without introducing extra overhead. Several optimization options are also considered. Simulation is conducted on the success rate of finding node disjoint paths.
7116226
Energy-Efficient Connected Coverage of Discrete Targets in Wireless Sensor Networks
A major concern in wireless sensor networks is to maximize network lifetime (in terms of rounds) while maintaining a high quality of services (QoS) at each round such as target coverage and network connectivity. Due to the power scarcity of sensors, a mechanism that can efficiently utilize energy has a great impact on extending network lifetime. Most existing works concentrate on scheduling sensors between sleep and active modes to maximize network lifetime while maintaining target/area coverage and network connectivity. This paper generalizes the sleep/active mode by adjusting sensing range to maximize total number of rounds and presents a distributed heuristic to address this problem.