Recently Viola et al. have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce and empirically analysis two extensions to their approach: Firstly, a novel set of rotated haar-like features is introduced. These novel features significantly enrich the simple features of [6] and can also be calculated efficiently. With the...

Due to the semantic gap, the low-level features are unsatisfactory for object categorization. Besides, the use of semantic related image representation may not be able to cope with large inter-class variations and is not very robust to noise. To solve these problems, in this paper, we propose a novel object categorization method by using the sub-semantic space based image representation. First,...

AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine SVM , neural networks NN , naı̈ve Bayes, and k-nearest neighbor kNN . This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple tw...

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 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 ...

In this paper, we present a soft-computing approach to improve the accuracy in recognizing the state of the liver based on a clinical ultrasound image. The detection of regions of interest (ROIs), which significantly reveal liver aberrance, remains a challenge since the image quality is relatively low in the real-world cases. Instead of using a single ROI, in this work, the liver area is divide...

We extend the framework of Adaboost so that it builds a smoothed decision tree rather than a neural network. The proposed method, “Adatree 2”, is derived from the assumption of a probabilistic observation model. It avoids the problem of over-fitting that appears in other tree-growing methods by reweighing the training examples, rather than splitting the training dataset at each node. It differs...

An approach to automatically extract pertinent subsets of soft output classifiers, assumed to decision rules, is presented in this paper. They are aggregated into a global decision scheme using the Choquet integral. A selection scheme is defined that discards weak or redundant decision rules, keeping only the most relevant subset. An experimental study, based on real world data attest the inter...