Add to ORKGAbstract—SoftBoost is a recently presented boosting algorithm, which trades off the size of achieved classification margin and generalization performance. This paper presents a performance evaluation of SoftBoost algorithm on the generic object recog-nition problem. An appearance-based generic object recognition model is used. The evaluation experiments are performed using a difficult object recognition benchmark. An assessment with re-spect to different degrees of label noise as well as a comparison to the well known AdaBoost algorithm is performed. The obtained results reveal that SoftBoost is encouraged to be used in cases when the training data is known to have a high degree of noise. Otherwise, using Adaboost can achieve better perform...
In this paper we present a novel framework for generic object class detection by integrating Kernel ...
—In this paper, we present a large-scale examination of different appearance-based, segmentation-fre...
Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theor...
SoftBoost is a recently presented boosting algorithm, which trades off the size of achieved classifi...
The object recognition problem has challenged the computer vision community for long time due to the...
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object...
Boosting is a technique of combining a set weak classifiers to form one high-performance prediction ...
This paper deals about AdaBoost algorithm, which is used to create a strong classification function ...
This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a mu...
As a machine learning method, AdaBoost is widely applied to data classification and object detection...
The main aim of this thesis is to introduce a new improved AdaBoost algorithm based on the trad...
There are many studies on the application of boosting in image processing, such as face recognition,...
AbstractObject detection in natural scene and image is playing an important role in computer vision....
Abstract The Adaboost (Freund and Schapire, Eur. Conf. Comput. Learn. Theory 23–37, 1995) chooses a ...
Typical object detection systems work by training a classifier on features extracted at different sc...
In this paper we present a novel framework for generic object class detection by integrating Kernel ...
—In this paper, we present a large-scale examination of different appearance-based, segmentation-fre...
Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theor...
SoftBoost is a recently presented boosting algorithm, which trades off the size of achieved classifi...
The object recognition problem has challenged the computer vision community for long time due to the...
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object...
Boosting is a technique of combining a set weak classifiers to form one high-performance prediction ...
This paper deals about AdaBoost algorithm, which is used to create a strong classification function ...
This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a mu...
As a machine learning method, AdaBoost is widely applied to data classification and object detection...
The main aim of this thesis is to introduce a new improved AdaBoost algorithm based on the trad...
There are many studies on the application of boosting in image processing, such as face recognition,...
AbstractObject detection in natural scene and image is playing an important role in computer vision....
Abstract The Adaboost (Freund and Schapire, Eur. Conf. Comput. Learn. Theory 23–37, 1995) chooses a ...
Typical object detection systems work by training a classifier on features extracted at different sc...
In this paper we present a novel framework for generic object class detection by integrating Kernel ...
—In this paper, we present a large-scale examination of different appearance-based, segmentation-fre...
Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theor...