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This article presents a method aiming at quantifying the visual similarity between an image and a class model. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to label a self-organizing map (SOM) to measure image similarity. To manage this goal, we feed local signatures associated to the regions of interest into...
In this paper, we propose a novel method for robustly classifying visual concepts. In order to achieve this aim, we propose a scheme that relies on Self Organizing Maps (SOM [6]). Heterogeneous local signatures are first extracted from training images and projected into specialized SOM networks. The extracted signatures activate several neural maps producing activation histograms. These activation...
The main focus of this paper is to propose a new supervised farm classification method from remotely sensed Landsat7 ETM images and based on the kernel-adatron (KA) algorithm. This algorithm produces the separation of two farm classes by an optimal decision boundary defined by a linear separating hyperplane in a general feature space. Nonlinearities are handled by mapping the input data into a multidimensional...
In this paper, a high-level optimization methodology is applied for the implementation of the well-known convolutional face finder (CFF) algorithm for real-time applications on cellular phone, such as teleconferencing, advanced user interfaces, pictures indexing and security access control. This face detector is based on a feature extraction and classification technique which consists in a pipeline...
This article presents a method aiming at quantifying the visual similarity between two images. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to use self-organizing feature maps (SOM) to measure image similarity. To reach this goal, we feed local signatures associated to salient patches into the neural network...
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