A feature fusion based localized multiple kernel learning system for real world image classification

A feature fusion based localized multiple kernel learning system for real world image classification Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of real-world images is highly complex so they cannot be linearly classified. The kernel trick is efficacious to classify them. This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification. By using multiple kernels extracted from multiple features, we address the first challenge. To provide a solution for the second challenge, we use the idea of a localized MKL by assigning separate local weights to each kernel. We employed spatial pyramid match (SPM) representation of images and computed kernel weights based on Χ 2kernel. Experimental results demonstrate that our proposed model has achieved promising results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EURASIP Journal on Image and Video Processing Springer Journals

A feature fusion based localized multiple kernel learning system for real world image classification

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s).
Subject
Engineering; Signal,Image and Speech Processing; Image Processing and Computer Vision; Biometrics; Pattern Recognition
eISSN
1687-5281
D.O.I.
10.1186/s13640-017-0225-y
Publisher site
See Article on Publisher Site

Abstract

Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of real-world images is highly complex so they cannot be linearly classified. The kernel trick is efficacious to classify them. This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification. By using multiple kernels extracted from multiple features, we address the first challenge. To provide a solution for the second challenge, we use the idea of a localized MKL by assigning separate local weights to each kernel. We employed spatial pyramid match (SPM) representation of images and computed kernel weights based on Χ 2kernel. Experimental results demonstrate that our proposed model has achieved promising results.

Journal

EURASIP Journal on Image and Video ProcessingSpringer Journals

Published: Nov 29, 2017

References

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