http://hdl.handle.net/10453/37628 2026-04-10T19:47:53Z http://hdl.handle.net/10453/34288 Title: Decentralized Integral Controllability Analysis Based on a New Unconditional Stability Criterion Authors: Su, SW; Savkin, AV; Guo, Y; Celler, BG; Nguyen, HT 2015-01-01T00:00:00Z http://hdl.handle.net/10453/30035 Title: Exploratory Sound Analysis: Sonifying data about sound Authors: Ferguson, S; Cabrera, D Editors: SUSINI, P; WARUSFEL, O Abstract: Exploratory Sound Analysis: Sonifying data about sound 2008-01-01T00:00:00Z http://hdl.handle.net/10453/29986 Title: Domain Transfer SVM for Video Concept Detection Authors: Duan, L; Tsang, I; Xu, D; Maybank, S Editors: Irfan Essa, Sing Bing Kang, Marc Pollefeys Abstract: Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods. 2009-01-01T00:00:00Z http://hdl.handle.net/10453/29989 Title: Face detection from few training examples Authors: Shen, C; Paisitkriangkra, S; Zhang, J Editors: N/A Abstract: Face detection in images is very important for many multimedia applications. Haar-like wavelet features have become dominant in face detection because of their tremendous success since Viola and Jones [1] proposed their AdaBoost based detection system. While Haar features' simplicity makes rapid computation possible, its discriminative power is limited. As a consequence, a large training dataset is required to train a classifier. This may hamper its application in scenarios that a large labeled dataset is difficult to obtain. In this work, we address the problem of learning to detect faces from a small set of training examples. In particular, we propose to use co- variance features. Also for better classification performance, linear hyperplane classifier based on Fisher discriminant analysis (FDA) is proffered. Compared with the decision stump, FDA is more discriminative and therefore fewer weak learners are needed. We show that the detection rate can be significantly improved with covariance features on a small dataset (a few hundred positive examples), compared to Haar features used in current most face detection systems. 2008-01-01T00:00:00Z