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Information-Based Learning of Deep Architectures for Feature Extraction

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 8157))

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Abstract

Feature extraction is a crucial phase in complex computer vision systems. Mainly two different approaches have been proposed so far. A quite common solution is the design of appropriate filters and features based on image processing techniques, such as the SIFT descriptors. On the other hand, machine learning techniques can be applied, relying on their capabilities to automatically develop optimal processing schemes from a significant set of training examples. Recently, deep neural networks and convolutional neural networks have been shown to yield promising results in many computer vision tasks, such as object detection and recognition. This paper introduces a new computer vision deep architecture model for the hierarchical extraction of pixel–based features, that naturally embed scale and rotation invariances. Hence, the proposed feature extraction process combines the two mentioned approaches, by merging design criteria derived from image processing tools with a learning algorithm able to extract structured feature representations from data. In particular, the learning algorithm is based on information-theoretic principles and it is able to develop invariant features from unsupervised examples. Preliminary experimental results on image classification support this new challenging research direction, when compared with other deep architectures models.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, Via Roma 56, I-53100, Siena, Italy

    Stefano Melacci, Marco Lippi, Marco Gori & Marco Maggini

Authors
  1. Stefano Melacci

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  2. Marco Lippi

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  3. Marco Gori

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  4. Marco Maggini

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Editor information

Editors and Affiliations

  1. Department of Applied Science, University of Naples Parthenope, Centro Direzionale Isola C4, 80133, Napoli, Italy

    Alfredo Petrosino

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© 2013 Springer-Verlag Berlin Heidelberg

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Melacci, S., Lippi, M., Gori, M., Maggini, M. (2013). Information-Based Learning of Deep Architectures for Feature Extraction. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41184-7_11

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