TENSOR OBJECT CLASSIFICATION VIA MULTILINEAR DISCRIMINANT ANALYSIS NETWORK

If one con- siders an existing recognition model, using some given type of local descriptors [SM96, LG96, SC96] the range of application is limited by the complexity of the images

The conducted experiments showed that QPCANet outperforms RGB PCANet, Gray PCANet, RGB LDANet, RGB SIFT, and RGB LBP in various color image classification tasks, such as

One-class classifiers are particularly of interest for un- known object detection. Those classifiers are trained on a single-class dataset, and are expected to reject objects that

The first task is the object of Material Flow Analysis (MFA), while the second is more directly related to the logic of Life Cycle Analysis (LCA). One of the major challenges here

En particulier, nous démontrons l’apport unique des gravimètres cryogéniques à l’étude des modes propres sismiques les plus graves et aux modes sub-sismiques, tels que

We propose a method to identify the best subset of features and the corresponding classifier to achieve a complete geographic object-based image analysis. Two

• A sparse scattering network architecture, illustrated in Figure 1, where the classification is performed over a sparse code computed with a single learned dictionary of

Ces résultats nous laissent constater que le complexe formé entre le Fe(III) et le peroxyde d’hydrogène en milieu acide peut également être excité sous irradiation solaire