ABSTRACT
Object recognition is an important task in the computer vision field as it has many applications, including optical character recognition and facial recognition. However, many existing methods have demonstrated relatively poor performance in all but the most simple cases. Scale-invariant feature transform (SIFT) features attempt to alleviate issues surrounding complex examples involving variances in scale, rotation and illumination, but suffer, potentially, from the way the algorithm describes the keypoints it detects in images. Genetic programming (GP) is used for the first time in an attempt to find the optimal way of describing the image keypoints extracted by the SIFT algorithm. Training and testing results show that the fittest program from a GP search can improve on the standard SIFT descriptors after only a few generations of a small population. While early results may not yet show major improvements over standard SIFT features, they do open the door for further research and experimentation.
- W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming - An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA, Jan. 1998. Google ScholarDigital Library
- H. Bay, T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. In In ECCV, volume 1, pages 404--417, 2006. Google ScholarDigital Library
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), volume 1 of CVPR '05, pages 886--893, Washington, DC, USA, June 2005. IEEE. Google ScholarDigital Library
- J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992. Google ScholarDigital Library
- K. Krawiec. Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines, 3(4): 329--343, Dec. 2002. Google ScholarDigital Library
- D. G. Lowe. Three-dimensional object recognition from single two-dimensional images. Artif. Intell., 31(3): 355--395, Mar. 1987. Google ScholarDigital Library
- D. G. Lowe. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision, 1999. Google ScholarDigital Library
- K. Meffert and N. Rotstan. JGAP: Java Genetic Algorithms Package. http://jgap.sourceforge.net/, 2012.Google Scholar
- K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10): 1615--1630, 2005. Google ScholarDigital Library
- D. J. Montana. Strongly typed genetic programming. Evol. Comput., 3(2): 199--230, June 1995. Google ScholarDigital Library
- K. Neshatian and M. Zhang. Dimensionality reduction in face detection: A genetic programming approach. In Proceeding of the 24th International Conference Image and Vision Computing New Zealand, IVCNZ '09, pages 391--396, Wellington, 23--25 Nov. 2009. IEEE.Google ScholarCross Ref
- K. Neshatian, M. Zhang, and P. Andreae. A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Transactions on Evolutionary Computation, 2011.Google Scholar
- D. I. Perrett and M. W. Oram. Visual recognition based on temporal cortex cells: viewer-centred processing of pattern configuration. Verlag der Zeitshcrift fure Naturforschung, Tubingen, 53c: 51--541, May 1998.Google Scholar
- R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contributions by J. R. Koza). Google ScholarDigital Library
- M. S. Sarfraz and O. Hellwich. Head pose estimation in face recognition across pose scenarios. In A. Ranchordas and H. Araujo, editors, VISAPP (1), pages 235--242. INSTICC - Institute for Systems and Technologies of Information, Control and Communication, 2008.Google Scholar
- T. Schinke. ImageAnalyzer. http://www.neotos.de/en/content/imageanalyzer, Feb. 2010.Google Scholar
- A. Song. Fast video analysis by genetic programming. In J. Wang, editor, 2008 IEEE World Congress on Computational Intelligence, pages 3237--3243, Hong Kong, 1--6 June 2008. IEEE Computational Intelligence Society, IEEE Press.Google ScholarCross Ref
- A. Song and V. Ciesielski. Fast texture segmentation using genetic programming. In R. S. et al., editor, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 2126--2133, Canberra, 8--12 Dec. 2003. IEEE Press.Google ScholarCross Ref
- M. Zhang. Improving object detection performance with genetic programming. International Journal on Artificial Intelligence Tools, 16(5): 849--873, 2007.Google ScholarCross Ref
- M. Zhang, V. B. Ciesielski, and P. Andreae. A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Applied Signal Processing, 2003(8): 841--859, July 2003. Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis. Google ScholarDigital Library
- M. Zhang, X. Gao, and W. Lou. A new crossover operator in genetic programming for object classification. IEEE Transactions on Systems, Man and Cybernetics, Part B, 37(5): 1332--1343, Oct. 2007. Google ScholarDigital Library
Index Terms
- Genetic programming for improving image descriptors generated using the scale-invariant feature transform
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