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Boosting Classifier Cascades
2010
Neural Information Processing Systems
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The problem of optimal and automatic design of a detector cascade is considered. A novel mathematical model is introduced for a cascaded detector. ...
A boosting algorithm, FCBoost, is proposed for fully automated cascade design. ...
Classifier cascades In this work, we seek the design of cascades that are provably optimal under (4). We start by introducing a mathematical model for a detector cascade. ...
dblp:conf/nips/SaberianV10
fatcat:cokkzfqj75hdfdea2ftnma4quy
Designing efficient cascaded classifiers
2010
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10
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We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages. The approach relies on the idea of optimizing soft cascades. ...
previous stage-specific classifier. ...
system by instead optimizing a surrogate cascade of soft-classifiers. ...
doi:10.1145/1835804.1835912
dblp:conf/kdd/RaykarKY10
fatcat:46klmbwqhjd3hjj3qy73bbmwda
Automatic Design of Cascaded Classifiers
[chapter]
2004
Lecture Notes in Computer Science
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This classifier is then "sliced" using dynamic programming into a cascade of classifiers in a nearly computation-cost-optimal fashion. ...
We propose to automatically build a cascade of classifiers, given just a family of weak classifiers a desired performance level and little more. ...
a cascade of classifiers. ...
doi:10.1007/978-3-540-27868-9_108
fatcat:73oahnggwza7pcjeygtv4nw6re
Optimally Training a Cascade Classifier
[article]
2010
arXiv
pre-print
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Cascade classifiers are widely used in real-time object detection. ...
Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and ...
Thus the nth node classifier uses the results of the weak classifiers associated with node n, but also those associated with the previous n − 1 node classifiers in the cascade. ...
arXiv:1008.3742v1
fatcat:utwuo6v7tbgzxaqhfikgdw5sy4
Interpretable Cascade Classifiers with Abstention
2019
International Conference on Artificial Intelligence and Statistics
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In this contribution, we develop a POMDPbased framework to learn cost-sensitive heterogeneous cascading systems. ...
In a cascade, at each stage, a classifier can either classify an input or reject it, and send it to the next classifiers [22, 3] . ...
The existing cascading classifiers are mostly focused on learning rejection function [21, 16, 15] assuming that the classifiers are available. ...
dblp:conf/aistats/ClertantSCH19
fatcat:p5jjbesavvamjj5fgqoywbsz6a
Linear Asymmetric Classifier for cascade detectors
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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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 ...
Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. ...
Viola and Jones (2004) built a cascade classifier that detected faces at video rate. Instead of designing a complex monolithic classifier, a cascade of simpler classifiers was used. ...
doi:10.1145/1102351.1102476
dblp:conf/icml/WuMR05
fatcat:m5j3kebdvzgj5dxutco3xygqqq
Face Recognition using Haar Cascade Classifier
2021
International journal of modern trends in science and technology
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ABSTRACT OBJECTIVE Using Haar Cascade Classifier to recognise and detect face on webcamera. ...
Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier. • Positive images -These images contain the images which we want our classifier ...
doi:10.46501/ijmtst070119
fatcat:zgvxwygaerdnbadkte2myjferq
Cascading an Emerging Pattern Based Classifier
[chapter]
2010
Lecture Notes in Computer Science
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Experimental results show that our cascade attains higher accuracy than other state-of-the-art classifiers, including one of the most accurate emerging pattern based classifier. ...
Additionally, we propose a new method for building cascades of emerging pattern classifiers, which combines the higher accuracy of classifying with higher support thresholds with the lower levels of abstention ...
-Cascade of classifiers. ...
doi:10.1007/978-3-642-15992-3_26
fatcat:pfmpwyuhonfxxpy3eojwaqsajm
Cascade Classifiers for Hierarchical Decision Systems
[chapter]
2010
Studies in Computational Intelligence
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The obtained tree-structure with groups of classifiers assigned to each of its nodes is called a cascade classifier. ...
., 2007b), as an example, we show that the confidence of such classifiers can be lower than the confidence of cascade classifiers. ...
Cascade Classifiers In this section, we show how to use the cascade λ-representation of S(d) to build cascade classifiers for S(d). ...
doi:10.1007/978-3-642-05177-7_12
fatcat:fzsezrn65bcrvarteeo72lqhme
Optimally combining a cascade of classifiers
2006
Document Recognition and Retrieval XIII
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The search procedure only updates the rejection thresholds (one for each constituent classier) in the cascade, consequently no new classifiers are added and no training is necessary. ...
We propose a novel search based approach to automatically combine multiple classifiers in a cascade to obtain the desired tradeoff between classification speed and classification accuracy. ...
Classifiers at the front of the cascade are fast and inaccurate while classifiers towards the end of the cascade are more accurate but slower. ...
doi:10.1117/12.643669
dblp:conf/drr/ChellapillaSS06
fatcat:jjswwl7py5az7pa5jboamhkqpq
OnionNet: Sharing Features in Cascaded Deep Classifiers
[article]
2016
arXiv
pre-print
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We propose to replace a monolithic network with our novel cascade of feature-sharing deep classifiers, called OnionNet, where subsequent stages may add both new layers as well as new feature channels to ...
Importantly, intermediate feature maps are shared among classifiers, preventing them from the necessity of being recomputed. ...
A popular remedy is to set up a cascade of multiple classifiers of increasing strength, called stages [35] . Recently, a pair of independent DNNs was used in a cascade [2, 21, 41] . ...
arXiv:1608.02728v1
fatcat:isv7nlx2dzbwfpzfscuz43roya
Improving Iris Recognition Accuracy via Cascaded Classifiers
[chapter]
2004
Lecture Notes in Computer Science
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Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost. ...
In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). ...
In this sense, the LFC also acts as another iris image quality classifier. Therefore the cascading system outperforms the individual classifiers in terms of accuracy. ...
doi:10.1007/978-3-540-25948-0_58
fatcat:h4uzxd6b4nfqxdl6c6ihqajz4i
LACBoost and FisherBoost: Optimally Building Cascade Classifiers
[article]
2010
arXiv
pre-print
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A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. ...
The cascade framework of Viola and Jones has become the de facto standard. ...
In terms of the cascade classifier, a few different approaches such as soft cascade [8] , dynamic cascade [9] , and multi-exit cascade [10] . We have used the multi-exit cascade in this work. ...
arXiv:1005.4103v1
fatcat:jaacgpwvlfa7bnzwc2q44m7cju
Improving the Generalization Capacity of Cascade Classifiers
2013
IEEE Transactions on Cybernetics
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On the other hand, similar to other classifier ensembles, cascade classifiers are likely to have high Vapnik-Chervonenkis (VC) dimension, which may lead to overfitting the training data. ...
The cascade classifier is a usual approach in object detection based on vision, since it successively rejects negative occurrences, e.g., background images, in a cascade structure, keeping the processing ...
VC-STYLE ANALYSIS ON A CASCADE OF LINEAR CLASSIFIERS Similar to other classifier ensembles, cascade classifiers are likely to have high overfitting the training data. ...
doi:10.1109/tcyb.2013.2240678
pmid:23757522
fatcat:mlhgkkvgujgwnjxrqlarbwspda
Cascaded multiple classifiers for secondary structure prediction
2000
Protein Science
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We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. ...
We show that it is possible to design classifiers that can highly discriminate the three classes ~H, E, C! ...
Architecture of the cascaded multiple classifier Prof. G stands for GOR; ~i! for information; ~p! ...
doi:10.1110/ps.9.6.1162
pmid:10892809
pmcid:PMC2144653
fatcat:xinmndmk2rc3zltgtr4vtbaw2e
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