An efficient classification of flower images with convolutional neural networks

  • Authors

    • M V.D. Prasad
    • B JwalaLakshmamma
    • A Hari Chandana
    • K Komali
    • M V.N. Manoja
    • P Rajesh Kumar
    • Ch Raghava Prasad
    • Syed Inthiyaz
    • P Sasi Kiran
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9857
  • Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Deep Learning, Flower Classification, Stochastic Pooling.

  • Abstract

    Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

  • References

    1. [1] Inthiyaz, Syed, B. T. P. Madhav, and P. V. V. Kishore. "Flower segmentation with level sets evolution controlled by colour, texture and shape features." Cogent Engineering, vol.4, no. 1, pp.1-15, (2017): 1323572.

      [2] Syed Inthiyaz, B. T. P Madhav, P. V. V. Kishore, Vamsi Krishna M., Sri Sai Ram Kumar M., Srikanth K. and Arun Teja B. "Flower Image Segmentation: A Comparison Between Watershed, Marker Controlled Watershed, And Watershed Edge Wavelet Fusion." ARPN Journal of Engineering and Applied Sciences, vol.11, no.15, pp.9382-9387, (2016).

      [3] Syed Inthiyaz, B. T. P Madhav, P. V. V. Kishore, “Pre-Informed Level Set for Flower Image Segmentationâ€, International Conference on Smart Computing and Informatics (SCI), March 2017.

      [4] Nilsback, M-E., and Andrew Zisserman. "A visual vocabulary for flower classification." In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 2, pp. 1447-1454. IEEE, 2006.

      [5] Zhou, Hailing, Jianmin Zheng, and Lei Wei. "Texture aware image segmentation using graph cuts and active contours." Pattern Recognition 46, no. 6 (2013): 1719-1733.https://doi.org/10.1016/j.patcog.2012.12.005.

      [6] Nilsback, Maria-Elena, and Andrew Zisserman. "Delving deeper into the whorl of flower segmentation." Image and Vision Computing 28, no. 6 (2010): 1049-1062.https://doi.org/10.1016/j.imavis.2009.10.001.

      [7] Hong, An-xiang, Gang Chen, Jun-li Li, Zhe-ru Chi, and Dan Zhang. "A flower image retrieval method based on ROI feature." Journal of Zhejiang University-Science A 5, no. 7 (2004): 764-772.https://doi.org/10.1631/jzus.2004.0764.

      [8] Najjar, Asma, and EzzeddineZagrouba. "Flower image segmentation based on color analysis and a supervised evaluation." In Communications and Information Technology (ICCIT), 2012 International Conference on, pp. 397-401. IEEE, 2012.https://doi.org/10.1109/ICCITechnol.2012.6285834.

      [9] Angelova, Anelia, Shenghuo Zhu, and Yuanqing Lin. "Image segmentation for large-scale subcategory flower recognition." In Applications of Computer Vision (WACV), 2013 IEEE Workshop on, pp. 39-45. IEEE, 2013.https://doi.org/10.1109/WACV.2013.6474997.

      [10] Valliammal, N., and S. N. Geethalakshmi. "Automatic recognition system using preferential image segmentation for leaf and flower images." Computer Science & Engineering 1, no. 4 (2011): 13.

      [11] Hsu, Tzu-Hsiang, Chang-Hsing Lee, and Ling-Hwei Chen. "An interactive flower image recognition system." Multimedia Tools and Applications 53, no. 1 (2011): 53-73.https://doi.org/10.1007/s11042-010-0490-6.

      [12] Nilsback, Maria-Elena, and Andrew Zisserman. "Automated flower classification over a large number of classes." In Computer Vision, Graphics & Image Processing, 2008. ICVGIP'08. Sixth Indian Conference on, pp. 722-729. IEEE, 2008.https://doi.org/10.1109/ICVGIP.2008.47.

      [13] Guru, D. S., Y. H. Sharath, and S. Manjunath. "Texture features and KNN in classification of flower images." IJCA, Special Issue on RTIPPR (1) (2010): 21-29.

      [14] Nilsback, Maria-Elena, and Andrew Zisserman. "Delving deeper into the whorl of flower segmentation." Image and Vision Computing 28, no. 6 (2010): 1049-1062.https://doi.org/10.1016/j.imavis.2009.10.001.

      [15] Thorp, K. R., and D. A. Dierig. "Color image segmentation approach to monitor flowering in lesquerella." Industrial crops and products 34, no. 1 (2011): 1150-1159.https://doi.org/10.1016/j.indcrop.2011.04.002.

      [16] Zou, Jie, and George Nagy. "Evaluation of model-based interactive flower recognition." In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2, pp. 311-314. IEEE, 2004.

      [17] Kim, Jung-Hyun, Rong-Guo Huang, Sang-Hyeon Jin, and Kwang-Seok Hong. "Mobile-based flower recognition system." In Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on, vol. 3, pp. 580-583. IEEE, 2009.https://doi.org/10.1109/IITA.2009.407.

      [18] F. Siraj, M. A. Salahuddin and S. A. M. Yusof, "Digital Image Classification for Malaysian Blooming Flower," 2010 Second International Conference on Computational Intelligence, Modelling and Simulation, Bali, 2010, pp. 33-38.https://doi.org/10.1109/CIMSiM.2010.92.

      [19] Z. Dong, X. Tian, Multi-level photo quality assessment with multi-view features, Neurocomputing. 168 (2015) 308-319.https://doi.org/10.1016/j.neucom.2015.05.095.

      [20] Z. Dong, X. Shen, H. Li, X. Tian, Photo quality assessment with DCNN that understands image well, in : Proceedings of the International Conference on MultiMedia Modeling (MMM), 2015, pp.524-535.https://doi.org/10.1007/978-3-319-14442-9_57.

      [21] X. Lu, Z. Lin, H. Jin, J. Yang, J. Wang, Rating pictorial aesthetics using deep learning, in: Proceedings of the ACM Conference on Multimedia, 2014, 457-466.

      [22] A. Krizhevsky, I.Sutskever, G.E. Hinton, ImageNet classification with deep convolution neural networks, in: Proceedings of the Annual Conference on Neural Information Processing System (NIPS), 2012, pp.1097-1105.

      [23] Y. Sun, X. Wang, X. Tang, Deep learning face representation from predicting 10,000 classes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1891-1898.https://doi.org/10.1109/CVPR.2014.244.

      [24] K. Jarrett, K. Kavukcuoglu, M. Ranzato, Y. LeCun, What is the best multi-stage architecture for object recognition, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2009, pp. 2146-2153.https://doi.org/10.1109/ICCV.2009.5459469.

      [25] H. Lee, R. Grosse, R. Ranganath, A.Y.Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the International Conference on Machine Learning (ICML), 2009, pp. 609-616.https://doi.org/10.1145/1553374.1553453.

      [26] Y. Bengio, Learning deep architectures for AI, Foundations and trends in Machine Learning, Vol. 2, No. 1, pp. 1-127, 2009.https://doi.org/10.1561/2200000006.

      [27] Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE , Vol. 86, No. 11, pp. 2278-2324, 1998.https://doi.org/10.1109/5.726791.

      [28] H. Lee, A. Battle, R. Raina and A. Y. Ng, Efficient sparse coding algorithms, In Advances in neural information processing systems, pp. 801-808, 2006.

      [29] R. Salakhutdinov and G. E. Hinton, Deep Boltzmann Machines, Proceedings of the International Conference on Artificial Intelligence and Statistics, Clearwater Beach, Florida USA, pp. 448-455, 2009.

      [30] Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature, vol. 521, No. 7553, pp. 436-444, 2015.https://doi.org/10.1038/nature14539.

      [31] H. Lee, R. Grosse, R. Ranganath, A.Y.Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the International Conference on Machine Learning (ICML), 2009, pp. 609-616.https://doi.org/10.1145/1553374.1553453.

      [32] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248-255.

      [33] A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems(NIPS), Lake Tahoe, Nevada, USA pp. 1097-1105, 2012.

      [34] Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature, vol. 521, No. 7553, pp. 436-444, 2015.https://doi.org/10.1038/nature14539.

      [35] Rao, G. Anantha, P. V. V. Kishore, D. Anil Kumar, and A. S. C. S. Sastry. "Neural network classifier for continuous sign language recognition with selfie video." Far East Journal of Electronics and Communications 17.1 (2017): 49.

      [36] Kishore, P. V. V., M. V. D. Prasad, Ch Raghava Prasad, and R. Rahul. "4-Camera model for sign language recognition using elliptical fourier descriptors and ANN." In Signal Processing and Communication Engineering Systems (SPACES), 2015 International Conference on, pp. 34-38. IEEE, 2015.

      [37] Kishore, P. V. V., A. S. C. S. Sastry, and A. Kartheek. "Visual-verbal machine interpreter for sign language recognition under versatile video backgrounds." In Networks & Soft Computing (ICNSC), 2014 First International Conference on, pp. 135-140. IEEE, 2014.https://doi.org/10.1109/CNSC.2014.6906696.

      [38] Kishore, P. V. V., S. R. C. Kishore, and M. V. D. Prasad. "Conglomeration of hand shapes and texture information for recognizing gestures of Indian sign language using feed forward neural networks." International Journal of engineering and Technology (IJET) 5, no. 5 (2013): 3742-3756.

      [39] Rao, G. Ananth, and P. V. V. Kishore. "Selfie video based continuous Indian sign language recognition system." Ain Shams Engineering Journal (2017).

      [40] K. V. V. Kumar, P. V. V. Kishore, and D. Anil Kumar, “Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion,†Mathematical Problems in Engineering, vol. 2017, Article ID 6204742, 18 pages, 2017. https://doi.org/10.1155/2017/6204742.

  • Downloads

  • How to Cite

    V.D. Prasad, M., JwalaLakshmamma, B., Hari Chandana, A., Komali, K., V.N. Manoja, M., Rajesh Kumar, P., Raghava Prasad, C., Inthiyaz, S., & Sasi Kiran, P. (2017). An efficient classification of flower images with convolutional neural networks. International Journal of Engineering & Technology, 7(1.1), 384-391. https://doi.org/10.14419/ijet.v7i1.1.9857

    Received date: 2018-03-04

    Accepted date: 2018-03-04

    Published date: 2017-12-21