As the usage and accessing of children to the web resources with porn images contain is growing, requirement of methods with high accuracy to detect and block adult images is a necessity. In this paper, a novel multi-classifier scheme is proposed based on low-level feature to exploit of advantages in classifier ensemble for achieving better accuracy compared to single classifier that applied to...

Viola and Jones [1] proposed the influential rapid object detection algorithm. They used AdaBoost to select from a large pool a set of simple features and constructed a strong classifier of the form {j αjhj(x) ≥ θ} where each hj(x) is a binary weak classifier based on a simple feature. In this paper, we construct, using statistical detection theory, a binary decision tree from the strong classi...

Well known for its simplicity and effectiveness in classification, AdaBoost, however, suffers from overfitting when class-conditional distributions have significant overlap. Moreover, it is very sensitive to noise that appears in the labels. This paper tackles the above limitations simultaneously via optimizing a modified loss function (i.e., the conditional risk). The proposed approach has the...

As more large-scale camera systems are deployed around the world, the need for video privacy is becoming increasingly vital. State of the art face and people tracking systems are not yet sufficiently robust for this domain, due to changing lighting conditions, occlusions, and the need to be realtime. This has motivated us to instead track visual markers worn by individuals who wish to have thei...

During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for ehealth systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public doma...

Recent research in classification problems has mostly concentrated on ensemble methods that construct a set of base classifiers instead of a single classifier. An unlabeled instance is then classified by taking a vote of the base classifiers’ predictions of its class label. Ensemble methods like Bagging and AdaBoost have been shown to outperform the individual base classifiers when the base ind...

Breast cancer is one of the most dangerous, leading and widespread cancers in the world especially in women. For breast analysis, digital mammography is the most suitable tool used to take mammograms for detection of cancer. It has been proved in the literature that if it can be detected at early and initial stages, then there are many chances to cure timely and efficiently. Therefore, initial ...

Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. However, Breiman raised important questions about the margin explanation by developing a boosting alg...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...