Image Flower Recognition based on a New Method for Color Feature Extraction

Amira Ben Mabrouk, Asma Najjar, Ezzeddine Zagrouba

Abstract

In this paper, we present, first, a new method for color feature extraction based on SURF detectors. Then, we proved its efficiency for flower image classification. Therefore, we described visual content of the flower images using compact and accurate descriptors. These features are combined and the learning process is performed using a multiple kernel framework with a SVM classifier. The proposed method has been tested on the dataset provided by the university of oxford and achieved better results than our implementation of the method proposed by Nilsback and Zisserman (Nilsback and Zisserman, 2008) in terms of classification rate and execution time.

References

  1. Abdul Kadir, Lukito Edi Nugroho, A. S. P. I. S. (2011). Leaf classification using shape, color, and texture features. International Journal of Computer Trends and Technology, 2:225-230.
  2. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. (2008). Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU), 110:346-359.
  3. Chai, Y., Lempitsky, V., and Zisserman, A. (2011). Bicos: A bi-level co-segmentation method for image classification. In IEEE International Conference on Computer Vision, pages 2579-2586.
  4. Guru, D. S., Sharath, Y. H., and Manjunath, S. (2010). Texture features and knn in classification of flower images. IJCA,Special Issue on RTIPPR, pages 21-29.
  5. Juan, L. and Gwun, O. (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing, 3:143-152.
  6. Krishna Singh, Indra Gupta, S. G. (2010). Svm-bdt pnn and fourier moment technique for of leaf shape. International Journal of Signal Processing, Image Processing and Pattern Recognition, 3:67-78.
  7. Louradour, J., Daoudi, K., and Bach, F. (2007). Feature space mahalanobis sequence kernels: Application to svm speaker verification. IEEE Transactions on Audio, Speech and Language Processing, 15:2465-2475.
  8. Low, D. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60:91-110.
  9. Najjar, A. and Zagrouba, E. (2012). Flower image segmentation based on color analysis and a supervised evaluation. In International Conference on Communications and Information Technology (ICCIT), volume 2, pages 397-401.
  10. Nilsback, M.-E. and Zisserman, A. (2007). Delving into the whorl of flower segmentation. In Proceedings of the British Machine Vision Conference, volume 1, pages 570-579.
  11. Nilsback, M.-E. and Zisserman, A. (2008). Automated flower classification over a large number of classes. In Indian Conference on Computer Vision, Graphics and Image Processing, pages 722-729.
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Paper Citation


in Harvard Style

Ben Mabrouk A., Najjar A. and Zagrouba E. (2014). Image Flower Recognition based on a New Method for Color Feature Extraction . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 201-206. DOI: 10.5220/0004636302010206


in Bibtex Style

@conference{visapp14,
author={Amira Ben Mabrouk and Asma Najjar and Ezzeddine Zagrouba},
title={Image Flower Recognition based on a New Method for Color Feature Extraction},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={201-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004636302010206},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Image Flower Recognition based on a New Method for Color Feature Extraction
SN - 978-989-758-004-8
AU - Ben Mabrouk A.
AU - Najjar A.
AU - Zagrouba E.
PY - 2014
SP - 201
EP - 206
DO - 10.5220/0004636302010206