Keywords Assignment to Fixed Image Region Segmentations Using Fuzzy SVMs

Abstract:

The basic idea of this paper is multiple keywords can be assigned to image through the method of fixed region segmentation. We divide a single image into the 4-level regions. For each of them, the combined feature is extracted and inputted into the trained Fuzzy SVMs to classify, which has been proved better than conventional SVMs in the generalization ability. The values of classification in each category are calculated. Based on these values, the keywords are assigned.

• Periodical: Advanced Materials Research (Volume 429)
• Pages: 236-241
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.429.236
• Online since: January 2012
• Authors: Ye Ji, Yan Chen, Yan Cao, Li Li Qu
• Keywords: Content-Based Image Retrieval, Fuzzy Support Vector Machine (FSVM), Image Classification

References

[1] H.L. WAN, & U.C. Morshed, Image Semantic Classification by Using SVM, Journal of Software, Vol 14, No. 11, pp.1891-1899, (2003).

[2] Hong, P., Tian, Q. and Huang, T. S. Incorporate Support Vector Machines to content-based image retrieval with relevance feedback, Proceedings of the IEEE International Conference on Image Processing, Vancouver, Canada, pp.750-753, September (2000).

DOI: 10.1109/icip.2000.899563

[3] Xiang Sean Zhou, Thomas S. Huang. Relevance feedback in image retrieval: A comprehensive review, Multimedia Systems, Vol 8, No. 6, pp.536-544, (2003).

DOI: 10.1007/s00530-002-0070-3

[4] Simon Tong, Edward Chang. Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, Ottawa, Canada, p.107 – 118, September, (2001).

DOI: 10.1145/500141.500159

[5] Chapelle O, Haffner P, Vapnik V. SVMs for histogram-based image classification,. IEEE Transactions on Neural Network, special issue on Support Vectors, Vol 10, No. 5, pp.1055-1065, (1999).

DOI: 10.1109/72.788646

[6] Ye Ji, Yan Chen. Multiple Keywords Assignment to Images Using SVMs, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, China, Kunming, pp.2569-2573, July. (2008).

DOI: 10.1109/icmlc.2008.4620841

[7] S. Abe and T. Inoue. Fuzzy support vector machines for multiclass problems", In Proceedings of 10th European Symposium on Artificial Neural Networks (ESANN, 2002), Bruges, Belgium, p.113–118, April (2002).

[8] Park DK, Jeon YS, Won CS. Efficient use of local edge histogram descriptor, Proceedings of the ACM Workshops on Multimedia, USA, Los Angeles, pp.51-54, November (2000).

DOI: 10.1145/357744.357758

[9] B.S. Manjunath, J-R Ohm, Vinod V. Vasudevan, Akio Yamada. Color and Texture Descriptors, IEEE Transactions on Circuits and Systems for Video Technology, Vol 11, No. 6, pp.703-715, (2001).

DOI: 10.1109/76.927424

[10] He D. C and Wang L. Texture features based on texture spectrum, Pattern Recognition, Vol 24, No. 5, pp.1187-1195, (1991).

DOI: 10.1016/0031-3203(91)90144-t

[11] He D.C. and Wang L. Texture unit, texture spectrum and texture analysis, IEEE Trans. Geoscience and Remote Sensing, Vol 28, No. 4, pp.509-512, (1990).

DOI: 10.1109/tgrs.1990.572934

[12] Karkanis S, Galousi K, Maroulis D. Classification of endoscopic images based on texture spectrum, Workshop on Machine Learning in Medical Applications, p.63~69, (1999).

[13] Nello Cristianini, John Shawe-taylor. An introduction to Support Vector Machiness and other kernel-based learning method, Cambridge University Press, (2000).

DOI: 10.1017/cbo9780511801389