Prototype-based classification for image analysis and its application to crop disease diagnosis

Advances in Intelligent Systems and Computing

Volumn 428, Issue , 2016, Pages 329-339

  Mwebaze, Ernest ^a   Biehl, Michael ^b  

^a MAKERERE UNIVERSITY   (Uganda)

^b UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

DLVQ; Feature extraction; GLVQ; GMLVQ; Image analysis; LVQ; Multi class classification; Prototype based classification

Indexed keywords

COMPUTER AIDED DIAGNOSIS; CONFORMAL MAPPING; CROPS; DIAGNOSIS; FEATURE EXTRACTION; IMAGE PROCESSING; MOBILE DEVICES; PLANTS (BOTANY); SELF ORGANIZING MAPS; VECTOR QUANTIZATION;

DLVQ; GLVQ; GMLVQ; LVQ; MULTI-CLASS CLASSIFICATION; PROTOTYPE-BASED CLASSIFICATIONS;

IMAGE ANALYSIS;

EID: 84955236729     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-28518-4_29     Document Type: Conference Paper

References (13)

1. Neural Networks Research Centre, Bibliography on the self-organizing map (SOM), learning vector quantization (LVQ): University of Technology, Helsinki (2002). http://liinwww.ira.uka.de/bibliography/Neural/SOM.LVQ.html
2. Aduwo, J.R., Mwebaze, E., Quinn, J.A.: Automated vision-based diagnosis of cassava mosaic disease. In: Perner, P. (ed.) Industrial Conference on Data Mining—Workshops, pp. 114-122. IBaI Publishing (2010)
3. Mwebaze, E., Schneider, P., Schleif, F.-M., Aduwo, J.R., Quinn, J.A., Haase, S., Villmann, T., Biehl, M.: Divergence based classification in learning vector quantization. Neural Comput. 74(9), 1429-1435 (2011)
4. Sato, A.S., Yamada, K.: Generalized learning vector quantization. In: Mozer, M. C., Touretzky, D.S., Hasselmo, M.E. (eds.) NIPS, vol. 8, pp. 423-429. MIT Press, Cambridge (1996)
5. Schneider, Petra, Biehl, Michael, Hammer, Barbara: Adaptive relevance matrices in learning vector quantization. Neural Comput. 21(12), 3532-3561 (2009)
6. Khosla, A., Xiao, J., Torralba, A., Oliva, A.: Memorability of image regions. In: Pereira, F., Burges, C.J.C., Bottou, L.,Weinberger,K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 296-304. Curran Associates Inc (2012)
7. van de Weijer, J., Schmid, C., Verbeek, J.: Learning color names from real-world images. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1-8 (2007)
8. Khan, R., van deWeijer, J., Shahbaz Khan, F., Muselet, D., Ducottet, C., Barat, C.: Discriminative color descriptors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2866-2873 (2013)
9. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360-3367 (2010)
10. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169-2178 (2006)
11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91-110 (2004)
12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886-893 (2005)
13. Papari, G., Bunte, K., Biehl, M.: Waypoint averaging and step size control in learning by gradient descent (technical report), volume MLR-2011-06 of Machine Learning Reports, pp. 16-26. University of Bielefeld (2011)