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Machine Vision, Image Recognition and Machine Learning

[Evolutionary Discriminative Learning in Vision]

Paper:

Lei Zhang* and David Zhang, Evolutionary Cost-Sensitive Discriminative Learning With Application to Vision and Olfaction, IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 2, pp. 198-211, 2017. [paper]

Abstract:

In the design of machine learning models, one often assumes the same loss, which, however, may not hold in cost-sensitive learning scenarios. In a face-recognition-based access control system, misclassifying a stranger as a house owner and allowing entry may result in a more serious financial loss than misclassifying a house owner as a stranger and not allowing entry. That is, different types of recognition mistakes may lead to different losses, and therefore should be treated carefully. It is expected that a cost-sensitive learning mechanism can reduce the total loss when given a cost matrix that quantifies how severe one type of mistake is against another one. However, in many realistic applications, the cost matrix is unknown and unclear to users. Motivated by these concerns, in this paper, we propose an evolutionary cost-sensitive discriminative learning (ECSDL) method, with the following merits: 1) it addresses the definition of cost matrix in cost-sensitive learning without human intervention; 2) an evolutionary backtracking search algorithm is derived for the NP-hard cost matrix optimization; and 3) a costsensitive discriminative subspace is found, where the betweenclass separability and within-class compactness are well achieved, such that recognition becomes easier. Experiments in a variety of cost-sensitive vision and olfaction classification tasks demonstrate the efficiency and effectiveness of the proposed ECSDL approach.

The proposed Algorithm:

Experimental Results

Face Analysis: Face Recognition

Odor Analysis: Gas Recognition

References:

[1] L. Zhang and D. Zhang, “Evolutionary Cost-sensitive Extreme Learning Machine,” IEEE Trans. Neural Networks and Learning Systems, vol. 28, no. 12, pp. 3045-3060, 2017.

[2] T. V. Nguyen, S. Liu, B. Ni, J. Tan, Y. Rui, and S. Yan, “Sense beauty via face, dressing, and/or voice,” in Proc. 20th ACM Int. Conf. Multimedia, 2012, pp. 239–248.

[3] H. Yan, “Cost-sensitive ordinal regression for fully automatic facial beauty assessment,” Neurocomputing, vol. 129, pp. 334–342, Apr. 2014.

[4] M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in Proc. Int. Conf. Comput. Vis. (ICCV), Nov. 2011, pp. 543–550.