This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a metalearning scheme aimed at improving fusion of lowand high-level speech information. While some existing systems fuse several classifier outputs, the proposed method uses a selective fusion scheme that takes into account conveying channel, speaking style...

in recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. in sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their lik...

Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor...

This paper describes our participation to the TRECVID 2009 challenge [5]. This year, we focused on an optimized fusion by means of Genetic Algorithm. We have implemented a classical approach for concept detection in video shots based on low-level and intermediate features extraction, supervised classifiers and fusion process. We compare the genetic fusion with usual late fusion (sum or weighted...

AdaBoost algorithms fuse weak classifiers to be a strong classifier by adaptively determine fusion weights of weak classifiers. In this paper, an enhanced AdaBoost algorithm by adjusting inner structure of weak classifiers (ISABoost) is proposed. In the traditional AdaBoost algorithms, the weak classifiers are not changed once they are trained. In ISABoost, the inner structures of weak classifi...

The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. This is because a combined use is supposed to improve the results of classification tasks compared to single-data use. This paper addressed using of combination of hyperspectral and Light Detection And Ranging (LIDAR) data in classification field. This paper ...

We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extract features from input images and convert them into mass different frames of discernment. A fusion module then aggregates these using Dempster’s rule. An end-to-end learning procedure allows us fine-tune the overall a...

Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers’ outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outp...