We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based clas...

In this paper, generalized versions of two ensemble methods for regression based on variants of the original AdaBoost algorithm are proposed. The generalization of these regression methods consists in restricting the unit simplex for the weights of the instances to a smaller set of weighting probabilities. Various imprecise statistical models can be used to obtain a restricted set of weighting ...

Robust and fast human detection in static image is very important for real applications. Although different feature descriptors have been proposed for human detection, for HOG descriptor, how to select and combine more distinguish block-based HOGs, and how to simultaneously make use of the correlation and the local information of these selected HOGs still lack enough research and analysis. In t...

We present a margin-based bound for ranking in a general setting, using the L∞ covering number of the hypothesis space as our complexity measure. Our bound suggests that ranking algorithms that maximize the ranking margin will generalize well. We produce a Smooth Margin Ranking algorithm, which is a modification of RankBoost analogous to Approximate Coordinate Ascent Boosting. We prove that thi...

Several important issues involved in Adaboost-cascade learning still remain open problems. In this work, several novel ideas are proposed for improved Adaboost-cascade object detection. The most important one is the novel Topology Oriented Adaboost (TOBoost) algorithm. TOBoost immediately minimizes the classification error of each selected feature, and thus enables the final detector to be more...

For face recognition, face feature selection is an important step. Better features should result in better performance. This paper describes a robust face recognition algorithm using multiple face region features selected by the AdaBoost algorithm. In conventional face recognition algorithms, the face region is dealt with as a whole. In this paper we show that dividing a face into a number of s...

The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With ...

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robus...

This paper is about a generalization of ensemble methods for regression which are based on variants of the basic AdaBoost algorithm. The generalization of these regression methods consists in restricting the unit simplex for the weights of the instances to a smaller set of weighting probabilities. The proposed algorithms cover the standard AdaBoost-based regression algorithms and standard regre...

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only and the weak classifiers use a simple decision stump....