Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify ...

We develop a novel approach to explain why AdaBoost is successful classifier. By introducing measure of the influence noise points (ION) in training data for binary classification problem, we prove that there strong connection between ION and test error. further identify decreases as iteration number or complexity base learners increases. confirm it impossible obtain consistent classifier witho...

AdaBoost is an important algorithm in machine learning and is being widely used in object detection. AdaBoost works by iteratively selecting the best amongst weak classifiers, and then combines several weak classifiers to obtain a strong classifier. Even though AdaBoost has proven to be very effective, its learning execution time can be quite large depending upon the application e.g., in face d...

Adaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a algorithm with only moderate accuracy guarantees. These have been designed to work in typical supervised settings and hence use labeled training data along base form ensemble. However, significant knowledge about the solution space might be available data. The convergence rate of AdaBoos...

In this paper, human face recognition of still images has been proposed. The proposed system involves five steps: face detection by AdaBoost face detector, region of interest (ROI) extraction, feature extraction using discrete wavelet transform (DWT), dimensionality reduction by employing independent component analysis (ICA) and classification using k-Nearest Neighborhood (k-NN) classifier. Exp...

We propose an ensemble learning method called Network Boosting which combines weak learners together based on a random graph (network). A theoretic analysis based on the game theory shows that the algorithm can learn the target hypothesis asymptotically. The comparison results using several datasets of the UCI machine learning repository and synthetic data are promising and show that Network Bo...

This paper presents a comparative study on five feature selection heuristics applied to a retinal image database called DRIVE. Features are chosen from a feature vector (encoding local information, but as well information from structures and shapes available in the image) constructed for each pixel in the field of view (FOV) of the image. After selecting the most discriminatory features, an Ada...

This paper proposes a robust real-time artificial landmarks detection and recognition system for indoor mobile robot. First, histograms of oriented gradient (HOG) features are extracted to resolve the illumination changes in indoor environment. Second, AdaBoost based algorithm is used in detection phase to increase the processing speed. Finally, RBF-SVM classifier is used for recognition. Exper...

This paper describes a vision-based pedestrian detection system for robots, and autonomous vehicles. For that purpose the Haar-like features were used to discriminate pedestrians. Those features were used as input in a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields an extremely efficient classifier. The proposed syste...