Spatial encoding of visual words for image classification

Dong Liu; Shengsheng Wang; Fatih Porikli

doi:10.1117/1.JEI.25.3.033008

31 May 2016 Spatial encoding of visual words for image classification

Dong Liu, Shengsheng Wang, Fatih Porikli

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 3, 033008 (May 2016). https://doi.org/10.1117/1.JEI.25.3.033008

Abstract

Appearance-based bag-of-visual words (BoVW) models are employed to represent the frequency of a vocabulary of local features in an image. Due to their versatility, they are widely popular, although they ignore the underlying spatial context and relationships among the features. Here, we present a unified representation that enhances BoVWs with explicit local and global structure models. Three aspects of our method should be noted in comparison to the previous approaches. First, we use a local structure feature that encodes the spatial attributes between a pair of points in a discriminative fashion using class-label information. We introduce a bag-of-structural words (BoSW) model for the given image set and describe each image with this model on its coarsely sampled relevant keypoints. We then combine the codebook histograms of BoVW and BoSW to train a classifier. Rigorous experimental evaluations on four benchmark data sets demonstrate that the unified representation outperforms the conventional models and compares favorably to more sophisticated scene classification techniques.

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Dong Liu, Shengsheng Wang, and Fatih Porikli "Spatial encoding of visual words for image classification," Journal of Electronic Imaging 25(3), 033008 (31 May 2016). https://doi.org/10.1117/1.JEI.25.3.033008

Published: 31 May 2016

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KEYWORDS

Visualization

Image classification

Computer programming

Data modeling

Image visualization

Visual process modeling

Performance modeling

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