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This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
The classification of remotely sensed images knows a large progress seen the availability of images of different resolutions as well as the abundance of the techniques of classification. Moreover a number of works showed promising results by the fusion of spatial and spectral information. For this purpose we propose a methodology allowing to combine this two information to refine an SVM classification,...
A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is...
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that the generative model structure in the classifier is correct, and (ii) Likelihood Space Classification with the assumption that the generative model structure...
The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they...
In this article we propose different fusion schemes using information provided by visible and infrared images for road obstacle SVM-based classification. Three approaches for the fusion of VIS and IR information are presented. The early fusion yields a feature vector integrating at the feature level both visual and infrared information. The obtained bimodal feature vector is used as input to an SVM-based...
In this paper, the multi-kernel SVM (Support Vector Machine) classification, integrated with a fusion process, is proposed to segment brain tumor from multi-sequence MRI images (T2, PD, FLAIR). The objective is to quantify the evolution of a tumor during a therapeutic treatment. As the procedure develops, a manual learning process about the tumor is carried out just on the first MRI examination. Then...
Most recent methods for image classification focus on how to formulate different types of features effectively in a uniform formula. Although these features take on different importance for image classification, most previous work gives the same weight to the features when they are combined. In this paper, we propose an approach to integrate multi-features by following the multiple kernel learning...
Biometric solution for embedded device gained significant attention in the commercial and research sectors over recent years. Combining multiple biometrics may enhance the performance of personal verification system in accuracy and reliability. This paper presents a new multi-biometric verification solution aimed at implementing on an embedded system within a wide range of applications. The system...
We propose a fusion model at data-level based on a linear combination of kernels for an SVM-based classification. The kernel functions are evaluated on disjoint entries, on the signature acquired from the visible and infrared spectrum. Different feature extraction and feature selection algorithms have been investigated in order to compute different feature vectors. A bi-objective optimization (using...
The algorithm based on multi-feature and SVM is proposed. The paper firstly uses wavelet de-noising for gait images. The text offers to use width descriptors as gait features and combines lower angle features. The kernel-based Fisher criterion and support vector machine is combined to classification and identification. The gait characteristic is extracted by KFDA, which can obtain the best projection...
Since inadequacy information from spectral characteristics for very high resolution remote sensing multispectral imagery segmentation/classification, we propose the combination of spectral feature which was extracted by a variable mean shift clustering algorithm and spatial features by Gabor filter banks and support vector machine is employed to achieve feature fusion and classification. Some issues...
According to the global and local features of images, Fourier descriptor and other multi-features is introduced for SVMs classifier. At first, extracting features of images is done, then classification method of SVMs for recognition is discussed. Experimentation with 11 image groups is conducted and the results prove that Fourier descriptors are simple, efficient, and effective for recognition of...
We present a hierarchical feature fusion model for image classification that is constructed by an evolutionary learning algorithm. The model has the ability to combine local patches whose location, width and height are automatically determined during learning. The representational framework takes the form of a two-level hierarchy which combines feature fusion and decision fusion into a unified model...
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