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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...
The adaptive-subspace self-organizing map (ASSOM) is useful for invariant feature generation and visualization. However, the learning procedure of the ASSOM is slow. In this paper, two fast implementations of the ASSOM are proposed to boost ASSOM learning based on insightful discussions of the basis rotation operator of ASSOM. We investigate the objective function approximately maximized by the classical...
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...
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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