Flower image classification with basket of features and multi layered artificial neural networks

  • Abstract
  • Keywords
  • References
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  • Abstract

    Artificial intelligence is penetrating most of the classification and recognition tasks performed by a computer. This work proposes to classify flower images based on features extracted during segmentation and after segmentation using multiple layered neural networks. The segmentation models used are watershed, wavelet, wavelet fusion model, supervised active contours based on shape, color and Local binary pattern textures and color, fused textures based active contours. Multi-dimension feature vectors are constructed from these segmented results for each indexed flower image labelled with their name. Each feature becomes input to a neuron in various feature layers and error back propagation algorithm with convex optimization structure trains these multiple feature layers. Testing with different flower images sets from multiple sources resulted in average classification accuracy of 92% for shape, color and texture supervised active contour segmented flower images.


  • Keywords

    Flower Image Segmentation; Pattern Classification; Multi-Dimensional Features; Artificial Neural Networks.

  • References

      [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 Segmentation”, International Conference on Smart Computing and Informatics (SCI), March 2017.

      [4] Pont-Tuset, Jordi, Pablo Arbelaez, Jonathan T. Barron, Ferran Marques, and Jitendra Malik. "Multiscale combinatorial grouping for image segmentation and object proposal generation." IEEE transactions on pattern analysis and machine intelligence 39, no. 1 (2017): 128-140.

      [5] Zhang, Hui, Jason E. Fritts, and Sally A. Goldman. "Image segmentation evaluation: A survey of unsupervised methods." computer vision and image understanding 110, no. 2 (2008): 260-280.

      [6] Ilea, Dana E., and Paul F. Whelan. "Image segmentation based on the integration of colour–texture descriptors—A review." Pattern Recognition 44, no. 10 (2011): 2479-2501.

      [7] Li, Ying, Wei Rao, Jing Peng, Ying Du, Linzhi Meng, and Zheng Gu. "Egress Mechanism Color Image Segmentation Based on Region and Feature Fusion in Mars Exploration." In 3rd International Symposium of Space Optical Instruments and Applications, pp. 301-308. Springer, Cham, 2017.

      [8] Wu, Jian Kang, Mohan S. Kankanhalli, Joo-Hwee Lim, and Dezhong Hong. "Color Feature Extraction." Perspectives on Content-Based Multimedia Systems (2000): 49-67.

      [9] 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.

      [10] 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.

      [11] Nilsback, Maria-Elena, and Andrew Zisserman. "Delving deeper into the whorl of flower segmentation." Image and Vision Computing 28, no. 6 (2010): 1049-1062.

      [12] 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.

      [13] Najjar, Asma, and Ezzeddine Zagrouba. "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.

      [14] 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.

      [15] 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.

      [16] 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.

      [17] 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.

      [18] 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.

      [19] Nilsback, Maria-Elena, and Andrew Zisserman. "Delving deeper into the whorl of flower segmentation." Image and Vision Computing 28, no. 6 (2010): 1049-1062.

      [20] 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.

      [21] 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.

      [22] 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.

      [23] 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.

      [24] P. V. V. Kishore, M. V. D. Prasad, D. A. Kumar, and A. S. C. S. Sastry, “Optical Flow Hand Tracking and Active Contour Hand Shape Features for Continuous Sign Language Recognition with Artificial Neural Networks,” 2016 IEEE 6th International Conference on Advanced Computing (IACC), Feb. 2016.

      [25] P. V. V. Kishore, D. A. Kumar, Goutham E.N.D, and M. Manikanta, “Continuous sign language recognition from tracking and shape features using Fuzzy Inference Engine,” 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Mar. 2016.

      [26] Haralick, Robert M., and Karthikeyan Shanmugam. "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 3, no. 6 (1973): 610-621.

      [27] Gómez, Walter, W. C. A. Pereira, and Antonio Fernando C. Infantosi. "Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound." IEEE transactions on medical imaging 31, no. 10 (2012): 1889-1899.

      [28] Bianconi, Francesco, and Antonio Fernández. "Evaluation of the effects of Gabor filter parameters on texture classification." Pattern Recognition 40, no. 12 (2007): 3325-3335.

      [29] Wu, Qinggang, Yong Gan, Bin Lin, Qiuwen Zhang, and Huawen Chang. "An active contour model based on fused texture features for image segmentation." Neurocomputing 151 (2015): 1133-1141.

      [30] Khare, Manish, Rajneesh Kumar Srivastava, and Ashish Khare. "Object tracking using combination of daubechies complex wavelet transform and Zernike moment." Multimedia Tools and Applications 76, no. 1 (2017): 1247-1290.

      [31] De Bom, Clécio R., Elisângela L. Faria, P. Marcelo, P. Marcio, Maury D. Correia, and Rodrigo Surmas. "Multiscale Matching of Micro-CT images using Pattern Recognition and Hu moments." NOTAS TÉCNICAS 4, no. 1 (2016).

      [32] N. Kim, “Euclidian distance minimization of probability density functions for blind equalization”, in Journal of Communications and Networks, vol. 12, no. 5, pp. 399-405, Oct. 2010.

      [33] V. N. Kumar and K. V. L. Narayana, “Development of an ANN-Based Pressure Transducer”, in IEEE Sensors Journal, vol. 16, no. 1, pp. 53-60, Jan.1, 2016.

      [34] S. K. Gharghan, R. Nordin, M. Ismail and J. A. Ali, “Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling” in IEEE Sensors Journal, vol. 16, no. 2, pp. 529-541, Jan.15, 2016.

      [35] Kishore, P. V. V., Kishore, S. R. C., & Prasad, M. V. D. (2013). 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(5), 3742-3756.

      [36] G. A. Rao, P. V. V. Kishore, D. A. 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, vol. 17, no. 1, pp. 49–71, Mar. 2017.

      [37] P. V. V. Kishore, A. S. C. S. Sastry, and A. Kartheek, “Visual-verbal machine interpreter for sign language recognition under versatile video backgrounds,” 2014 First International Conference on Networks & Soft Computing (ICNSC2014), Aug. 2014.

      [38] 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, pp. 1–18, 2017.

      [39] P. V. V. Kishore, D. A. Kumar, A. S. C. S. Sastry, and E. K. Kumar, “Motionlets Matching with Adaptive Kernels for 3D Indian Sign Language Recognition,” IEEE Sensors Journal, pp. 1–1, 2018.

      [40] K. kumar Eepuri, P. V. V. Kishore, S. A S C S, T. K. K. Maddala, and A. kumar Dande, “Training CNNs for 3D Sign Language Recognition with color texture coded Joint Angular Displacement Maps,” IEEE Signal Processing Letters, pp. 1–1, 2018.




Article ID: 10795
DOI: 10.14419/ijet.v7i1.1.10795

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