Beyond SIFT for Image Categorization by Bag-of-Scenes Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Sébastien Paris , Xanadu Halkias , Hervé Glotin

ABSTRACT

In this paper, we address the general problem of image/object categorization with a novel approach referred to as Bag-of-Scenes (BoS). Our approach is efficient for both low semantic applications, such as texture classification and higher semantic tasks such as natural scenes recognition. It is based on the widely used combination of (i) Sparse coding (Sc), (ii) Max-pooling and (iii) Spatial Pyramid Matching (SPM) techniques applied to histograms of multi-scale Local Binary/Ternary Patterns (LBP/LTP) as local features. This approach can be considered as a two-layer hierarchical architecture. The first layer encodes quickly the local spatial patch structure via histograms of LBP/LTP, while the second layer encodes the relationships between pre-analyzed LBP/LTP-scenes/objects. In order to provide comparative results, we also introduce an alternate 2-layer architecture. For this latter, the first layer is encoding directly the multi-scale Differential Vectors (DV) local patches instead of histograms of LBP/LTP. Our method outperforms SIFT-based approaches using Sc techniques and can be trained efficiently with a simple linear SVM. Our BoS method achieves \(87.46\,\%\), and \(90.35\,\%\) of accuracy for Scene-15, UIUC-Sport datasets respectively. More... »

PAGES

191-207

References to SciGraph publications

  • 2011-07. On the Occurrence Probability of Local Binary Patterns: A Theoretical Study in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • 2004-05. Robust Real-Time Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. Improving the Fisher Kernel for Large-Scale Image Classification in COMPUTER VISION – ECCV 2010
  • 2006. Description of Interest Regions with Center-Symmetric Local Binary Patterns in COMPUTER VISION, GRAPHICS AND IMAGE PROCESSING
  • 2009-09. Are Gabor phases really useless for face recognition? in PATTERN ANALYSIS AND APPLICATIONS
  • 2001-05. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007. Face Detection Based on Multi-Block LBP Representation in ADVANCES IN BIOMETRICS
  • 2011. Face Image Retrieval Using Sparse Representation Classifier with Gabor-LBP Histogram in INFORMATION SECURITY APPLICATIONS
  • 2007. Learning Multi-scale Block Local Binary Patterns for Face Recognition in ADVANCES IN BIOMETRICS
  • 2011-03. Pegasos: primal estimated sub-gradient solver for SVM in MATHEMATICAL PROGRAMMING
  • Book

    TITLE

    Pattern Recognition Applications and Methods

    ISBN

    978-3-319-12609-8
    978-3-319-12610-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-12610-4_12

    DOI

    http://dx.doi.org/10.1007/978-3-319-12610-4_12

    DIMENSIONS

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    43 schema:description In this paper, we address the general problem of image/object categorization with a novel approach referred to as Bag-of-Scenes (BoS). Our approach is efficient for both low semantic applications, such as texture classification and higher semantic tasks such as natural scenes recognition. It is based on the widely used combination of (i) Sparse coding (Sc), (ii) Max-pooling and (iii) Spatial Pyramid Matching (SPM) techniques applied to histograms of multi-scale Local Binary/Ternary Patterns (LBP/LTP) as local features. This approach can be considered as a two-layer hierarchical architecture. The first layer encodes quickly the local spatial patch structure via histograms of LBP/LTP, while the second layer encodes the relationships between pre-analyzed LBP/LTP-scenes/objects. In order to provide comparative results, we also introduce an alternate 2-layer architecture. For this latter, the first layer is encoding directly the multi-scale Differential Vectors (DV) local patches instead of histograms of LBP/LTP. Our method outperforms SIFT-based approaches using Sc techniques and can be trained efficiently with a simple linear SVM. Our BoS method achieves \(87.46\,\%\), and \(90.35\,\%\) of accuracy for Scene-15, UIUC-Sport datasets respectively.
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