We submit the results of our system to the THUMOS’13 Challenge on Recognition Task. We apply a multi-modal approach to recognize actions on UCF101 dataset. These features include Dense Trajectories (motion features), SIFT (image features) and MFCC (audio features). Moreover, we also use the Fisher vector encoding, which is a state-of-theart feature representation on popular image classification...

The success and popularity of naive Bayes has led to a field of research exploring algorithms that seek to retain its numerous strengths while reducing error by alleviating the attribute interdependence problem. This thesis builds upon this promising field of research, contributing a systematic survey and several novel and effective techniques. It starts with a study of the strengths and weakne...

An implementation for the classification of remote sensing images with support vector machines (SVM) is introduced. This tool, called imageSVM, allows a user-friendly work, especially with large, highly-resolved data sets in the ENVI/IDL environment. imageSVM uses LIBSVM for the training of the SVM in combination with a user-defined grid search. Parameter settings can be set flexibly during the...

INTRODUCTION Graph analysis of the resting state fMRI signal can provide a functional connectivity viewpoint on how the ‘functional network’ as a whole behaves. The data are composed of correlations of time courses between different brain regions. Alterations in the functional network have been found in individuals with brain disorders or neurodegenerative diseases. Head motion and other data a...

Introduction Methods & Materials Results Conclusions Support Vector Machines have been proven to be very effective methods for classification and regression. However, in order to obtain good generalization errors the user needs to choose appropriate values for the involved parameters of the model. SVM model selection problem Tuning the hyperparameters : kernel parameters (γ, degree, coef, etc) ...

The creation and classification of segments for object based urban land cover mapping is the key goal of this master thesis. An algorithm based on region growing and merging was developed, implemented and tested. The synergy effects of a fused data set of SAR and optical imagery were evaluated based on the classification results. The testing was mainly performed with data of the city of Beijing...

Introduction: Tissue class segmentation is integral to many MRI analyses. The process is complicated by noise, scan gain parameters, and bias field. Modern segmentation approaches rely on location-based tissue class prior probability maps to estimate bias field and segment the brain, thereby necessitating an accurate spatial registration of the observed image to the template image of the priors...

We propose an approach to detect violence in movies at video shot level using low-level and mid-level features. We use audio energy, pitch and Mel-Frequency Cepstral Coefficients (MFCC) features to represent the affective audio content of movies. For the affective visual content, we extract average motion information. To learn a model for violence detection, we choose a discriminative classific...

Optical character Recognition (OCR) is an important application of machine learning where an algorithm is trained on a data set of known letters/digits and can learn to accurately classify letters/digits. A variety of algorithms have shown excellent accuracy for the problem of handwritten digits, 4 of which are looked at here. Additionally, we attempt to extend these techniques to the harder pr...

High-level feature extraction We describe our system for the task of HLF extraction in TRECVID 2008. Features at different granularities are extracted to describe visual content of keyframes, and four classifiers for each concept are trained by SVM, and different fusion strategies are used to combine final results. The brief introduction to each run is shown in the Table 1.1. Table 1.1. MAP and...