Image Classifying Based on Cost-Sensitive Layered Cascade Learning

Abstract:

Image Classification is an important means of image processing, Traditional research of image classification usually based on following assumptions: aiming for the overall classification accuracy, sample of different category has the same importance in data set and all the misclassification brings same cost. Unfortunately, class imbalance and cost sensitive are ubiquitous in classification in real world process, sample size of specific category in data set may much more than others and misclassification cost is sharp distinction between different categories. High dimension of eigenvector caused by diversity content of images and the big complexity gap between distinguish different categories of images are common problems when dealing with image Classification, therefore, one single machine learning algorithms is not sufficient when dealing with complex image classification contains the above characteristics. To cure the above problems, a layered cascade image classifying method based on cost-sensitive and class-imbalance was proposed, a set of cascading learning was build, and the inner patterns of images of specific category was learned in different stages, also, the cost function was introduced, thus, the method can effectively respond to the cost-sensitive and class-imbalance problem of image classifying. Moreover, the structure of this method is flexible as the layer of cascading and the algorithm in every stage can be readjusted based on business requirements of image classifying. The result of application in sensitive image classifying for smart grid indicates that this image classifying based on cost-sensitive layered cascade learning obtains better image classification performance than the existing methods.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 453-458
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.453
• Online since: December 2014
• Authors: Feng Huang*, Yun Liang, Li Huang, Ji Ming Yao, Wen Feng Tian
• Keywords: Cascade Learning, Class-Imbalance, Cost-Sensitive, Data Mining, Image Classifying

References

[1] Tong S, Chang E. Support vector machine active learning for image retrieval[C]. In: Proceedings of the 9th ACM International Conference on Multimedia. New York, USA: ACM, 2001. 107-118.

DOI: 10.1145/500141.500159

[2] Joshi A J, Porikli F, Papanikolopoulos N. Multi-class active learning for image classification[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 23-29.

DOI: 10.1109/cvpr.2009.5206627

[3] Breiman L Random forests[J]. Machine Learning, 2001 , 45 (1): 5-32.

[4] Chen Rong, Cao YongFeng, Sun Hong. Multi-class Image Classification with Active Learning and Semi-supervised Learning[J]. Acta Automatic sinica. 2011, 37(8): 954-962.

[5] Qi XiaoZhen, Wang qing. An Image Classification Approach Based on Sparse Coding and Multiple Kernel Learning[J]. Acta Electronica Sinica. 2012, 40(4): 773-779.

[6] Probost, Foster. Machine Leaming from Imbalaneed Data Sets[C]. Invited Paper for the AAAI'2000 Workshop on Imbalanced Data Sets. (2000).

[7] Chawla,N. V, JaPkowiez,N., KotgZ,A. Editorial: SPeeial Issue on Learning from Imbalanced Data Sets[C]. ACM SIGDKK Explorations Newsletter. 2004, 6(1): 1-6.

[8] Batista. GE., Prati R.C., Monard M.C. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data[C]. ACM SIGKDD Explorations Newsletter. 2004, 6(1): 20-29.

DOI: 10.1145/1007730.1007735