Face detection is critical to the application based on human face. Accurate and quick face detection has a determining effect on its following process, like face verification and face recognition etc. Adaboost algorithm [4] has been regarded as a very effective machine learning algorithm that can be applied to the face detection. However, with the increasing of image size determined by the adva...

Boosting algorithms have been widely used to tackle a plethora of problems. In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, ev...

This paper presents a method for an automated anatomical labeling of bronchial branches (ALBB) for augmented display of its result for bronchoscopy assistance. A method for automated ALBB plays an important role for realizing an augmented display of anatomical names of bronchial branches. The ALBB problem can be considered as a problem that each bronchial branch is classified into the bronchial...

Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer. Boosting-based transfer learning algorithms are a subset of such methods and have been applied successfully within the transfer learning community. In this paper, we address some of the weaknesses of such algorithms and extend the most popul...

This chapter gives insight in the use of Genetic-Based Machine Learning (GBML) for supervised tasks. Five GBML systems which represent different learning methodologies and knowledge representations in the GBML paradigm are selected for the analysis: UCS, GAssist, SLAVE, Fuzzy AdaBoost, and Fuzzy LogitBoost. UCS and GAssist are based on a non-fuzzy representation, while SLAVE, Fuzzy AdaBoost, an...

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to ...

This paper aims to use a large set of feature descriptions as geometric cues to build the structural knowledge of an indoor image. In this paper, a large quantity of training images are used to obtain the required information through learning. We apply a multi-class version of AdaBoost with weak learners based on the decision tree to label regions in an indoor image as “ground”, “wall” and “cei...