Recently, Viola and Jones [1] have proposed a detector using Adaboost to select and combine weak classifiers from a very large pool of weak classifiers, and it has been proven to be very successful for detecting faces. We have followed their approach and applied it to detect rear views of cars. The detector was carefully examined and was expanded in a number of ways, such as varying the type an...

In this work, we propose a face detection method based on the Gentle AdaBoost algorithm which is used for construction of binary tree structured strong classifiers. Gentle AdaBoost algorithm update values are constructed by using the difference of the conditional class probabilities for the given value of Haar features proposed by [1]. By using this approach, a classifier which can model image ...

This paper aims to propose cybercrime detection and prevention model by using Support Vector Machine (SVM) and AdaBoost algorithm in order to reduce data damaging due to running of malicious codes. The performance of this model will be evaluated on a Facebook dataset, which includes benign executable and malicious codes. The main objective of this paper is to find the effectiveness of different...

This paper introduces the CASIA audio emotion recognition method for the audio sub-challenge of Audio/Visual Emotion Challenge 2011 (AVEC2011). Two popular pattern recognition techniques, SVM and AdaBoost, are adopted to solve the emotion recognition problem. The feature set is also simply investigated by comparing the performance of classifier built on the baseline feature set and the dimensio...

Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost’s robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware Ada...

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. ...

Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The Data Replication Method was proposed as tool for solving the ODC problem using a singl...

In this paper, we propose a multi-modal voice activity detection system (VAD) that uses audio and visual information. In multi-modal (speech) signal processing, there are two methods for fusing the audio and the visual information: concatenating the audio and visual features, and employing audioonly and visual-only classifiers, then fusing the unimodal decisions. We investigate the effectivenes...

In this paper, we propose the post-classification scheme that is useful for improving weak-hypothesis combination of AdaBoost. The post-classification scheme allows the weak hypotheses to be combined nonlinearly, and can be shown to have a generally better performance than the original linear-combination approach in either theory or practice. The post-classification scheme provides a general pe...

This paper proposes a novel technique to exploit discriminative models with subclasses for speech recognition. Speech recognition using discriminative models has attracted much attention in the past decade. However, most discriminative models are still based on tree clustering results of HMM states. On the contrary, our proposed method, referred to as subclass AdaBoost, jointly selects optimal ...