Unsupervised Features Extraction Using a Multi-view Self Organizing Map for Image Classification

In the multimedia processing, the extraction and the representation of characteristics are considered as an important step. The extraction of the ideal characteristics having the ability to reflect the intrinsic content of the images as complete as possible is still a difficult problem in computer vision. Little research has focused on this problem. This paper presents a new unsupervised method based on the self-organizing map (SOM) for features extraction. Our method consists of two main steps: Extracting the sub-regions of an image according to their points of interest and using the SOM for the different views of an image such as color, texture and shape. Then, combine them to have finally a "Multi-View" vector characteristic. The proposed method is evaluated on three image classification datasets Cloud, Coil100 and CIFAR-10. The classification accuracies of the proposed method for the three datasets are much higher in comparison with the other methods cited in the literature.