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Boosting Classifier Cascades

Mohammad J. Saberian, Nuno Vasconcelos
2010 Neural Information Processing Systems  
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

Vikas C. Raykar, Balaji Krishnapuram, Shipeng Yu
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
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]

Etienne Grossmann
2004 Lecture Notes in Computer Science  
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]

Chunhua Shen, Peng Wang, Anton van den Hengel
2010 arXiv   pre-print
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

Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar
2019 International Conference on Artificial Intelligence and Statistics  
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

Jianxin Wu, Matthew D. Mullin, James M. Rehg
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
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

Arnav Madan
2021 International journal of modern trends in science and technology  
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]

Milton García-Borroto, José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa
2010 Lecture Notes in Computer Science  
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]

Zbigniew W. Raś, Agnieszka Dardzińska, Wenxin Jiang
2010 Studies in Computational Intelligence  
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

Kumar Chellapilla, Michael Shilman, Patrice Simard, Kazem Taghva, Xiaofan Lin
2006 Document Recognition and Retrieval XIII  
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]

Martin Simonovsky, Nikos Komodakis
2016 arXiv   pre-print
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]

Zhenan Sun, Yunhong Wang, Tieniu Tan, Jiali Cui
2004 Lecture Notes in Computer Science  
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]

Chunhua Shen and Peng Wang and Hanxi Li
2010 arXiv   pre-print
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

Oswaldo Ludwig, Urbano Nunes, Bernardete Ribeiro, Cristiano Premebida
2013 IEEE Transactions on Cybernetics  
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

Mohammed Ouali, Ross D. King
2000 Protein Science  
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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