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排序方式: 共有151条查询结果,搜索用时 15 毫秒
1.
In this work, a new method for the creation of classifier ensembles is introduced. The patterns are partitioned into clusters to group together similar patterns, a training set is built using the patterns that belong to a cluster. Each of the new sets is used to train a classifier. We show that the approach here presented, called FuzzyBagging, obtains performance better than Bagging. 相似文献
2.
Alessandra Lumini 《Pattern recognition》2007,40(3):1057-1065
Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user specific biometric feature (BioHashing) has received much attention. These methods have significant functional advantages over sole biometrics i.e. zero equal error rate. The main drawback of the base BioHashing method proposed in the literature relies in exhibiting low performance when an “impostor” B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we introduce some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an “impostor” steals the Hash key. 相似文献
3.
Alessandra Lumini 《Pattern recognition》2006,39(4):714-716
We present a system for fingerprint verification that approaches the problem as a two-class pattern recognition problem. The features extracted by “FingerCode” are used to capture the ridge strength. This feature vector is then classified as genuine or impostor according to a novel approach to handle the fingerprint verification as a two-class problem. Moreover, we show that extracting the features from sub-images around the core permits to better represent the local information. 相似文献
4.
Alessandra Lumini 《Pattern recognition》2006,39(3):495-497
The problem addressed in this paper is the template selection and update in biometrics based on clustering. Template selection is a reliable method to reduce the number of templates used in a biometric system to account for variations observed in a person's biometric data. An efficient method based on clustering with automatic selection of the number of clusters is proposed in this work for finding subgroups of similar templates which are used for prototype selection.Experimental results confirm the advantage of the new method and the importance of adopting a procedure to perform template selection. 相似文献
5.
Summary The synthesis of three Schiff-base type monomers containing pyrrole units was performed. Their polymerization was carried out by chemical oxidation with (NH4)2S2O8. Some preliminary thermal and electrical properties were determined. 相似文献
6.
We introduce a class of tree bimorphisms that define exactly the translations performed by syntax-directed translation schemata. We also show that these “quasi-alphabetic” tree bimorphisms preserve recognizability, and that their class is closed under composition and inverses. 相似文献
7.
We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from
the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform
and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is
based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific
knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero
equal error rate).
相似文献
Loris NanniEmail: |
8.
In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different
notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in
three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines
a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier.
Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously
published in the literature.
相似文献
Loris NanniEmail: |
9.
Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval
system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches
limit the utilization of the user’s feedback to a single search session, performing a short-term learning. In this paper we
present a novel approach for short and long term learning, based on the definition of an adaptive similarity metric and of
a high level representation of the images. For short-term learning, the relevant and non-relevant information given by the
user during the feedback process is employed to create a positive and a negative subspace of the feature space. For long-term
learning, the feedback history of all the users is exploited to create and update a representation of the images which is
adopted for improving retrieval performance and progressively reducing the semantic gap between low-level features and high-level
semantic concepts. The experimental results prove that the proposed method outperforms many other state of art methods in
the short-term learning, and demonstrate the efficacy of the representation adopted for the long-term learning.
相似文献
Annalisa FrancoEmail: |
10.
The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance. 相似文献