Autosoft Journal

Online Manuscript Access


Image Classification using Optimized MKL for sSPM


Authors



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.


Keywords


Pages

Total Pages: 10

DOI
10.31209/2018.100000010


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Online Article

Cite this document


References

S. B. Bucak, R. Jin, and A. K. Jain, (2014). Multiple kernel learning for a visual object recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1354-1369. https://doi.org/10.1109/TPAMI.2013.212

I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, (2015). Euclidean distance matrices: Essential theory, algorithms, and applications. IEEE Signal Processing Magazine, 32, 12-30. https://doi.org/10.1109/MSP.2015.2398954

B. Fernando, F. Elisa, D. Muselet, and M. Sebban, (2012). Discriminative feature fusion for image classification. IEEE Conference on Computer Vision and Pattern Recognition, 3434-3441. https://doi.org/10.1109/cvpr.2012.6248084

S. Ganguly, D. Bhattacharjee, and M. Nasipuri, (2017). Fuzzy matching of edge and curvature based features from range images for 3D face recognition. Intelligent Automation & Soft Computing, 23(1),51-62. https://doi.org/10.1080/10798587.2015.1121616

K. Grauman and T. Darrell. (2005). The pyramid match kernel: Discriminative classification with sets of image features. IEEE International Conference on Computer Vision, 2, 1458-1465. https://doi.org/10.1109/iccv.2005.239

Y. Gu, Q. Wang, X. Jia, and J. A. Benediktsson, (2014). A novel MKL model of integrating LiDAR data and MSI for urban area classification. IEEE Transactions on Geoscience and Remote Sensing, 52, 805-818.

T. Guha and R. K. Ward, (2014). Image similarity using sparse representation and compression distance. IEEE Transactions on Multimedia, 16, 980-987. https://doi.org/10.1109/TMM.2014.2306175

J. Hur, H. Lim, and A. S. Chui (2015). Generalized deformable spatial pyramid: Geometry-preserving dense correspondence estimation. IEEE Conference on Computer Vision and Pattern Recognition, 1392-1400. https://doi.org/10.1109/cvpr.2015.7298745

Y. Jiang and J. Ma, (2015). Combination features and models for human detection. IEEE Conference on Computer Vision and Pattern Recognition, 240-248.

U. Kanimozhi and D. Manjula, (2017).An intelligent incremental filtering feature selection and clustering algorithm for effective classification. Intelligent Automation & Soft Computing. https://doi.org/10.1080/10798587.2017.1307626

S. Lazebnik, C. Schmid, and J. Ponce, (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition, 2,2169-2178. https://doi.org/10.1109/cvpr.2006.68

F. Li and P. Pietro (2005).A bayesian hierarchical model for learning natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition, 2, 524-531.

S. Niazmardi, S. Homayouni, A. Safari, J. L. Shang, and H. McNairn, (2018). Multiple kernel representation and classification of multivariate satellite-image time series for crop mapping. International Journal of Remote Sensing, 39(1), 149-168.

Z. B. Pan, T. Ohmi, and K. Kotani, (2006). An efficient method of constructing L1-Type norm feature to estimate euclidean distance for fast vector quantization. Intelligent Automation & Soft Computing, 12(3), 269-274. https://doi.org/10.1080/10798587.2006.10642930

A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, (2008). Simple MKL. Journal of Machine Learning Research, 9, 2491-2521.

F. B. Silva, S. Goldenstein, S. Tabbone, and D. S. Torres, (2013). Image classification based on bag of visual graphs. 20th IEEE International Conference on Image Processing, 4312-4316. https://doi.org/10.1109/icip.2013.6738888

J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, (2014). Multiple kernel sparse representations for supervised and unsupervised learning. IEEE Transaction on Image Processing, 23, 1057-7149. https://doi.org/10.1109/tip.2014.2322938

J. T. Tsai, Y. Y. Lin, and H.Y.M. Liao, (2014). Per-cluster ensemble kernel learning for multi-modal image clustering with group-dependent feature selection. IEEE Transaction on Multimedia, 16,2229-2241. https://doi.org/10.1109/TMM.2014.2359769

M. Varma and D. Ray, (2007). Learning the discriminative power-invariance trade-off. IEEE International Conference on Computer Vision, 1-8. https://doi.org/10.1109/iccv.2007.4408875

M. Varma and B. R. Babu, (2009). More generality in efficient multiple kernel learning. International Conference on Machine Learning, 1065-1072. https://doi.org/10.1145/1553374.1553510

S. Yan, X. Xu, D. Xu, and S. Lin, (2015).Image classification with densely sampled image windows and generalized adaptive multiple kernel learning. IEEE Transactions on Cybernetics,45, 381-390. https://doi.org/10.1109/TCYB.2014.2326596

Y. Yang and S. Newsam, (2011).Spatial pyramid co-occurrence for image classification. IEEE International Conference on Computer Vision, 1465-1472. https://doi.org/10.1109/iccv.2011.6126403

G. Yue and K. Kataqishi, (2016). Improved spatial pyramid matching for sports image classification. IEEE International Conference on Semantic Computering, 32-38.

J. Yue, S. J. Mao, and M. Li, 2016). A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sensing Letters, 7(9), 875-884. https://doi.org/10.1080/2150704X.2016.1193793

H. Zhang, A. C. Berg, M. Maire, and J. Malik, (2006). SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. IEEE Conference on Computer Vision and Pattern Recognition, 2, 2126-2136. https://doi.org/10.1109/cvpr.2006.301

H.Q. Zhang, C. Xu, X. Gao, and L. Cao, (2012). An indoor mobile visual localization algorithm based on Harris-Sift. Intelligent Automation & Soft Computing, 18(7), 885-897. https://doi.org/10.1080/10798587.2012.10643296

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/