Multi-class Boosting for Early Classification of Sequences.
ABSTRACT We propose a new boosting algorithm for sequence classification, in particular one that enables early classification of multiple classes. In many practical problems, we would like to classify a sequence into one of K classes as quickly as possible, without waiting for the end of the sequence. Recently, an early classification boosting algorithm was proposed for binary classification that employs a weight propagation technique. In this paper, we extend this model to a multi-class early classification. The derivation is based on the loss function approach, and the developed model is quite simple and effective. We validated the performance through experiments with real-world data, and confirmed the superiority of our approach over the previous method.
Full-textDOI: · Available from: Hiroshi Sawada, Jun 30, 2015
- SourceAvailable from: Yoichi Sato
Conference Paper: Early facial expression recognition with high-frame rate 3D sensing.[Show abstract] [Hide abstract]
ABSTRACT: This work investigates a new challenging problem: how to exactly recognize facial expression as early as possible, while most works generally focus on improving the recognition rate of facial expression recognition. The features of facial expressions in their early stage are unfortunately very sensitive to noise due to their low intensity. So, we propose a novel wavelet spectral subtraction method to spatio-temporally refine the subtle facial expression features. Moreover, in order to achieve early facial expression recognition, we newly introduce an early AdaBoost algorithm for facial expression recognition problem. Experiments using our database established by using a high-frame rate 3D sensing showed that the proposed method has a promising performance on early facial expression recognition.Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, October 9-12, 2011; 01/2011
Conference Paper: Logitboost Extension for Early Classification of Sequences.[Show abstract] [Hide abstract]
ABSTRACT: We propose a new boosting method for classification of time sequences. In the problem of on-line classification, it is essential to classify time sequences as quickly as possible in many practical cases. This type of classification is called “early classification.” Recently, an Adaboost-based “Earlyboost” has been proposed, which is known for its efficiency. In this paper, we propose a Logitboost-based early classification for further improvements of Earlyboost. We demonstrate the structure of the proposed method, and experimentally verify its performance.Computer Analysis of Images and Patterns - 14th International Conference, CAIP 2011, Seville, Spain, August 29-31, 2011, Proceedings, Part I; 01/2011
Conference Paper: Early facial expression recognition using early RankBoost[Show abstract] [Hide abstract]
ABSTRACT: This work investigated a new challenging problem: how to recognize facial expressions as early as possible, in contrast to finding ways to improve the facial expression recognition rate. Unlike conventional facial expression recognition, early facial expression recognition is inherently difficult due to the initial low intensity of the expressions. To overcome this problem, a novel early recognition approach based on RankBoost is used to infer the facial expression category of an input facial expression sequence as early as possible. Facial expression intensity increases monotonically from neutral to apex in most cases, and this observation was elaborated for developing an early facial expression recognition method. To identify the most discriminative features of subtle facial expressions, weak rankers are used to learn the temporal variations of pairwise subtle facial expression features in accordance with their temporal order. Then, a weight propagation method is applied to boost a weak ranker into an early recognizer. Experiments on the Cohn-Kanade database and a custom-made dataset built using a high-speed motion capture system demonstrated that the proposed method has promising performance for early facial expression recognition.Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on; 01/2013