Abstract
In this paper, an improved classifier based on nearest feature line (NFL), shortest feature line segment (SFLS) and nearest feature center (NFC), called the feature cure metric (FCM), is proposed for hand gesture recognition and face recognition. Borrowing the concept from the NFC and SFLS classifiers, the proposed classifier uses the novel distance metric between the test sample and the pair of prototype samples. A large number of experiments on Jochen Triesch Static Hand Posture (JTSHP) Database, Yale face database and JAFFE face database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed approach achieves better recognition rate than the other well-known classifiers, such as nearest neighbor (NN) classifier, NFL classifier, nearest neighbor line (NNL) classifier, center-based nearest neighbor (CNN) classifier, extended nearest feature line (ENFL) classifier, SFLS classifier and NFC classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory 13(1), 21–27 (1967)
Li, S.Z., Lu, J.: Face recognition using the nearest feature line method. IEEE Trans. Neural Networks 10(2), 439–443 (1999)
Li, S.Z., Chan, K.L., Wang, C.L.: Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Trans. Pattern Anal. Machine Intell. 22(11), 1335–1339 (2000)
Zhou, Z., Kwoh, C.K.: The pattern classification based on the nearest feature midpoints. In: International Conference on Pattern Recognition, vol. 3, pp. 446–449 (2000)
Zheng, W., Zhao, L., Zou, C.: Locally nearest neighbor classifiers for pattern classification. Pattern Recognition 37(6), 1307–1309 (2004)
Gao, Q.B., Wang, Z.Z.: Center-based nearest neighbor classifier. Pattern Recognition 40(1), 346–349 (2007)
Zhou, Y.L., Zhang, C.S., Wang, J.C.: Extended nearest feature line classifier. In: Proc. of 8th Pacific Rim Int. Conf. Artificial Intelligence, Auckland, New Zealand, pp. 183–190 (August 2004)
Han, D.Q., Han, C.Z., Yang, Y.: A novel classifier based on shortest feature line segment. Pattern Recognition Letters 32(3), 485–493 (2011)
Feng, Q., Pan, J.S., Yan, L.: Nearest feature centre classifier for face recognition. Electronics letters 48(18), 1120–1122 (2012)
Feng, Q., Huang, C.-T., Yan, L.: Repsentation-based Nearest Feature Plane for Pattern Recognition. Journal of Information Hiding and Multimedia Signal Processing 4(3), 178–191 (2013)
Feng, Q., Pan, J.S., Yan, L.: Restricted Nearest Feature Line with Ellipse for Face Recognition. Journal of Information Hiding and Multimedia Signal Processing 3(3), 297–305 (2012)
Feng, Q., Pan, J.S., Yan, L.: Two classifiers based on nearest feature plane for recognition. In: Proc. of IEEE International Conference on Image Processing, Melbourne, Australia, pp. 3316–3319 (September 2013)
Triesch, J., Malsburg, C.: Jochen Triesch Static Hand Posture Database (1996), http://www.idiap.ch/resource/gestures/
The Yale faces database (2001), http://cvc.yale.edu/projects/yalefaces/yalefaces.html
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding Facial Expressions with Gabor Wavelets. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (April 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Feng, Q., Pan, JS., Pan, TS. (2014). Feature Curve Metric for Image Classification. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-07455-9_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07454-2
Online ISBN: 978-3-319-07455-9
eBook Packages: Computer ScienceComputer Science (R0)