Asymmetric Pruning for Learning Cascade Detectors

Cascade classifiers are one of the most important contributions to real-time object detection. Nonetheless, there are many challenging problems arising in training cascade detectors. One common issue is that the node classifier is trained with a symmetric classifier. Having a low misclassification error rate does not guarantee an optimal node learning goal in cascade classifiers, i.e., an extremely high detection rate with a moderate false positive rate. In this work, we present a new approach to train an effective node classifier in a cascade detector. The algorithm is based on two key observations: 1) Redundant weak classifiers can be safely discarded; 2) The final detector should satisfy the asymmetric learning objective of the cascade architecture. To achieve this, we separate the classifier training into two steps: finding a pool of discriminative weak classifiers/features and training the final classifier by pruning weak classifiers which contribute little to the asymmetric learning criterion (asymmetric classifier construction). Our model reduction approach helps accelerate the learning time while achieving the pre-determined learning objective. Experimental results on both face and car data sets verify the effectiveness of the proposed algorithm. On the FDDB face data sets, our approach achieves the state-of-the-art performance, which demonstrates the advantage of our approach.Comment: 14 page

Efficiently learning a detection cascade with sparse eigenvectors

Real-time object detection has many computer vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce greedy sparse linear discriminant analysis (GSLDA) for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with. Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample reweighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions (e.g., face detection) demonstrate that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that AdaBoost and similar approaches are not the only methods that can achieve high detection results for real-time object detection

Asymmetric Totally-corrective Boosting for Real-time Object Detection

Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stage-wise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.Comment: 14 pages, published in Asian Conf. Computer Vision 201

Generating Compact Tree Ensembles via Annealing

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic behavior of selecting the same tree again and again due to the way the loss is optimized. At the same time, Random Forest is not based on loss optimization and obtains a more complex and less interpretable model. In this paper we present a novel method for obtaining compact tree ensembles by growing a large pool of trees in parallel with many independent boosting threads and then selecting a small subset and updating their leaf weights by loss optimization. We allow for the trees in the initial pool to have different depths which further helps with generalization. Experiments on real datasets show that the obtained model has usually a smaller loss than boosting, which is also reflected in a lower misclassification error on the test set.Comment: Comparison with Random Forest included in the results sectio

Pattern Recognition Using AdaBoost

V této práci se zaobírá algoritmem AdaBoost, který slouží k vytvoření silné klasifikační funkce pomocí několika slabých klasifikátorů. Seznámíme se taktéž s modifikacemi AdaBoostu, a to Real AdaBoostem, WaldBoostem, FloatBoostem a TCAcu. Tyto modifikace zlepšují některé z vlastností algoritmu AdaBoost. Probereme některé vlastnosti příznaků a slabých klasifikátorů. Ukážeme si třídu úloh, pro které je algoritmus AdaBoost použitelný. Popíšeme implementaci knihovny obsahující zmíněné metody a uvedeme některé testy provedené na implementované knihovně.This paper deals about AdaBoost algorithm, which is used to create a strong classification function using a number of weak classifiers. We familiarize ourselves with modifications of AdaBoost, namely Real AdaBoost, WaldBoost, FloatBoost and TCAcu. These modifications improve some of the properties of algorithm AdaBoost. We discuss some properties of feature and weak classifiers. We show a class of tasks for which AdaBoost algorithm is applicable. We indicate implementation of the library containing that method and we present some tests performed on the implemented library.

Application of AdaBoost

V této práci jsou uvedeny základy klasifikace a rozpoznávání vzorů.  Zaměříme se především na algoritmus AdaBoost, který slouží k vytvoření silné klasifikační funkce pomocí několika slabých klasifikátorů.  Seznámíme se taktéž s některými modifikacemi AdaBoostu. Tyto modifikace zlepšují některé z vlastností AdaBoostu. Podíváme se taktéž na slabé klasifikátory a příznaky k nim použitelné. Zvláště se podíváme na Haarovy příznaky. Probereme možnosti použití zmíněných algoritmů a příznaků při rozpoznávání výrazu obličeje. Popíšeme si situaci mezi databázemi výrazů obličejů. Nastíníme možnou implementaci aplikace rozpoznávání výrazů obličeje.Basics of classification and pattern recognitions will be mentioned in this work. We will focus mainly on AdaBoost algorithm, which serves to create a strong classifier function by some weak classifiers. We shall get acquainted with some modifications of AdaBoost. These modifications improve some of AdaBoost attributes. We shall also look into weak classifiers and features applicable to them. We shall especially look into the Haar- likes features. We shall discus possibilities of using the mentioned algorithms and features in facial expression recognition. We shall describe the situation between facial expression databases. We shall draw out a possible implementation of application of facial expression recognition.