Authors:
Marco Vanetti
;
Ignazio Gallo
and
Angelo Nodari
Affiliation:
Università degli Studi dell’Insubria, Italy
Keyword(s):
Unsupervised Feature Learning, Self-organizing Map, Natural Images Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In recent years a great amount of research has focused on algorithms that learn features from unlabeled data.
In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features
useful for the problem of automatic natural images classification. In particular we use the SOM model to
learn single-layer features from the extremely challenging CIFAR-10 dataset, containing 60.000 tiny labeled
natural images, and subsequently use these features with a pyramidal histogram encoding to train a linear SVM
classifier. Despite the large number of images, the proposed feature learning method requires only few minutes
on an entry-level system, however we show that a supervised classifier trained with learned features provides
significantly better results than using raw pixels values or other handcrafted features designed specifically for
image classification. Moreover, exploiting the topological property of the SOM neural network, it is possible
to reduce the nu
mber of features and speed up the supervised training process combining topologically close
neurons, without repeating the feature learning process.
(More)