Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Hybrid coevolutionary algorithms vs. SVM algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Feature synthesized EM algorithm for image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Automated design of image operators that detect interest points
Evolutionary Computation
Intelligent technologies for investing: a review of engineering literature
Intelligent Decision Technologies
Evolutionary learning of local descriptor operators for object recognition
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An approach for moving object recognition based on BPR and CI
Information Systems Frontiers
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Immune multiobjective optimization algorithm for unsupervised feature selection
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Genetic programming as strategy for learning image descriptor operators
Intelligent Data Analysis
| Hi-index | 0.00 |
Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key to the performance of object recognition. In this paper, we propose a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The knowledge about the problem domain is incorporated in primitive features that are used in the synthesis of composite features by CGP using domain-independent primitive operators. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can discover good composite features to distinguish objects from clutter and to distinguish among objects belonging to several classes. The comparison with other classical classification algorithms is favorable to the CGP-based approach proposed in this paper.