Classification is a major task in many applications and in particular for automatic semantic-based video content indexing and retrieval. In this paper, we focus on the challenging task of classifier output fusion. It is a necessary step to efficiently estimate the semantic content of video shots from multiple cues. We propose to fuse the numeric information provided by multiple classifiers in t...

As past years we participated to the high-level feature extraction task and we pursued on the fusion of classifier outputs. New for this year is an experiment with a twodimensional Hidden Markov Model. Altogether we submitted seven runs. Three runs was based on the SVM model, another three was based on the HMM and one run was done by fusing HMM results with the SVM. To compile the runs, color a...

This paper addresses a simple and effective approach of face and face-part classifier fusion under Gaussian assumption, which is able to process heterogeneous visible wavelength (VW) and near infrared (NIR) image data. Evaluations using existing and publicly available AdaBoost-based individual classifiers on the recently released CASIA-V4 iris distance database of close-up portrait images as we...

Purpose: Multiple sclerosis is a progressive neurological disease and a cause of many disabilities in individuals. Set of symptoms and the chronic nature of this disease, together impair the mental state of the person and cause awidervariety of psychological symptoms worsening the disease. Therefor, this study aimed to evaluate the effectiveness of cognitive therapy based on mindfulness psy...

Multimodal biometric methods use different fusion techniques to avoid authentication problems such as noisy sensors data, nonuniversality, and unacceptable error rates. Fusion methods have been proposed in different levels such as feature level and classification level. In this paper, we propose two multimodal biometric authentication methods using ear and finger knuckle (FK) images. A method b...

The INESC-ID’s Spoken Language Systems Laboratory (LF) primary system developed for the Spoken Web Search task of the Mediaeval 2013 evaluation campaign consists of the fusion of six individual sub-systems exploiting 3 different language-dependent phonetic classifiers. For each phonetic classifier, an acoustic keyword spotting (AKWS) sub-system based on connectionist speech recognition and a dy...

In this paper, we propose novel spectrally weighted mel-frequency cepstral coefficient (WMFCC) features for emotion recognition from speech. The idea is based on the fact that formant locations carry emotion-related information, and therefore critical spectral bands around formant locations can be emphasized during the calculation of MFCC features. The spectral weighting is derived from the nor...

Ensemble is a representative technique for improving classification performance by combining a set of classifiers. It is required to maintain the diversity among base classifiers for effective ensemble. Conventional ensemble approaches construct various classifiers by estimating the similarity on the output patterns of them, and combine them with several fusion methods. Since they measure the s...

This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize ...