@Article{2018.100000010,
AUTHOR = {Lu Wu, Quan Liu, Ping Lou},
TITLE = {Image Classification Using Optimized MKL for SSPM},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {249--257},
URL = {http://www.techscience.com/iasc/v25n2/39654},
ISSN = {2326-005X},
ABSTRACT = {The scheme of spatial pyramid matching (SPM) causes feature ambiguity near 
dividing lines because it divides an image into different scales in a fixed manner. 
A new method called soft SPM (sSPM) is proposed in this paper to reduce 
feature ambiguity. First, an auxiliary area rotating around a dividing line in four 
orientations is used to correlate the feature relativity. Second, sSPM is 
performed to combine these four orientations to describe the image. Finally, an 
optimized multiple kernel learning (MKL) algorithm with three basic kernels for 
the support vector machine is applied. Specifically, for each level, a suitable 
kernel is selected to map the data that fall within the corresponding 
neighbourhood. In addition, a mixed-norm regularization formulation is 
optimized using MKL to solve the classification problem. The method proposed 
in this paper performs well when applied to the Caltech 101 and Scene 15
datasets. Experimental results are collected under various conditions. The 
results of sSPM are improved by nearly 4% compared with the existing 
experimental results.},
DOI = {10.31209/2018.100000010}
}