2017
DOI: 10.1186/s13640-017-0225-y
|View full text |Cite
|
Sign up to set email alerts
|

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

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 9 publications
(3 citation statements)
references
References 37 publications
0
3
0
Order By: Relevance
“…In this study, a localized multiple kernel learning (LMKL) algorithm [42], which has been claimed to achieve a higher accuracy compared with canonical MKL with classification problems, was applied with the Gaussian radial basis function (RBF) kernel, the polynomial kernel, the linear kernel, the hyperbolic tangent kernel, the Laplacian kernel, the Bessel kernel, the ANOVA RBF kernel, the spline kernel, and the string kernel, with kernel parameters optimized for each kernel. In the LMKL framework, multiple kernels are used instead of a single kernel, but local weights are computed for kernels in the training phase, unlike canonical MKL where fixed weights for kernels are computed in the training phase and the weighted sum of kernels is computed [60]. The second method was MKELM, whose main concept is based on MKL; details can be found in [43].…”
Section: Classification Proceduresmentioning
confidence: 99%
“…In this study, a localized multiple kernel learning (LMKL) algorithm [42], which has been claimed to achieve a higher accuracy compared with canonical MKL with classification problems, was applied with the Gaussian radial basis function (RBF) kernel, the polynomial kernel, the linear kernel, the hyperbolic tangent kernel, the Laplacian kernel, the Bessel kernel, the ANOVA RBF kernel, the spline kernel, and the string kernel, with kernel parameters optimized for each kernel. In the LMKL framework, multiple kernels are used instead of a single kernel, but local weights are computed for kernels in the training phase, unlike canonical MKL where fixed weights for kernels are computed in the training phase and the weighted sum of kernels is computed [60]. The second method was MKELM, whose main concept is based on MKL; details can be found in [43].…”
Section: Classification Proceduresmentioning
confidence: 99%
“…These classes are chosen due to low variability in image spatial structure. As the network is only looking for natural spatial structural similarity avoidance of classes which have a large intraclass variance compared to the overall interclass relationship (Zamani and Jamzad, 2017). With this in mind and due to some the additional classes having a smaller number of sequences, the number of training and testing instances was changed to suit, at 20 training and 10 testing.…”
Section: N-caltech Dataset Extendedmentioning
confidence: 99%
“…To handle this challenge, feature selection [4][5][6][7][8] and subspace learning [9,10] have been developed to obtain suitable feature representations. Feature selection is commonly used as a preprocessing step for classification, so most feature selection algorithms are only designed for better predictability, such as high prediction accuracy.…”
Section: Introductionmentioning
confidence: 99%