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Multivariate two dimensional singular spectrum analysis based fusion method for four view image based object classification
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AI
DOI：10.1007/s11042-023-15712-3.png)
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Abstract
En 中文
A common method to perform the object classification with different images being taken at different views is to extract the features from each image without performing the fusion. On the other hand, this paper proposes a multivariate two dimensional singular spectrum analysis (M2DSSA) based approach to fuse the features in different images together to perform the object classification. First, a four channel two dimensional signal is formed using four images taken at four different views. Second, the M2DSSA is applied to the four channel two dimensional signal. Next, the histogram of the oriented gradient (Hog) is computed on each channel of each M2DSSA component. Then, the selection of the M2DSSA components is performed based on the correlation coefficients among these Hogs and the fusion of these images is performed via the M2DSSA. Next, the Hog of each reconstructed image is recomputed and these Hogs are employed as the features for the support vector machine to perform the object classification. Our proposed method yields the classification accuracies at 92.5925% and 97.8723% for the images in the first dataset and the second dataset, respectively. Since the information of the objects in different images is fused together, the computer numerical simulation results show that the classification accuracies of our proposed method are higher than those of the baseline method without performing the fusion and those of the other fusion methods.
Keywords:
Multivariate two dimensional singular spectrum analysis
Correlation coefficient based component selection method
Multi-view images
Object classification
Histogram of oriented gradient
Support vector machine
Journal
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3
Papers:
1.9W
Citations:
3.2W