การแบ่งส่วนรูปภาพดอกไม้ด้วยการใช้ซาเลียนซีแมปร่วมกับการประยุกต์ใช้ปริภูมิสีเอชเอสวีและหน้ากากสี
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การจำแนกประเภทรูปภาพดอกไม้เป็นสิ่งที่ท้าทาย เนื่องจากความคล้ายคลึงกันทางกายภาพของดอกไม้ เทคนิคการแบ่งส่วนรูปภาพ (Image segmentation) สามารถลดความซับซ้อนขององค์ประกอบภายในพื้นหลังภาพ ทำให้การจำแนกประเภทรูปภาพดอกไม้มีประสิทธิภาพมากขึ้น งานวิจัยชิ้นนี้ได้นำเสนอแนวคิดการแบ่งส่วนรูปภาพ โดยอิงการใช้ประโยชน์จากซาเลียนซีแมป (Saliency map) ในการเลือกบริเวณที่สนใจภายในภาพ และการใช้ปริภูมิสีเอชเอสวี (HSV) ผนวกกับการใช้หน้ากากสี (Color mask) ในการช่วยลดรายละเอียดที่ไม่สำคัญภายในพื้นหลังของรูปภาพ ผลการทดลองแสดงให้เห็นว่าวิธีการที่นำเสนอให้ผลลัพธ์การแบ่งส่วนรูปภาพโดยวัดจากค่าเฉลี่ย IoU เท่ากับ 54% (ซึ่งมากกว่างานวิจัยก่อนหน้า 13 %) ในขณะที่ค่าความถูกต้อง ความแม่นยำ ค่าความครบถ้วน และค่า F1 เมื่อจำแนกประเภทดอกไม้ด้วยแบบจำลอง VGG16 ที่ผ่านการปรับโครงสร้างเท่ากับ 87 %
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เอกสารอ้างอิง
P. Panwar, G. Gopal, and R. Kumar, "Image Segmentation using K-means clustering and Thresholding," Image, vol. 3, no. 05, pp. 1787-1793, 2016.
T.-W. Chen, Y.-L. Chen, and S.-Y. Chien, "Fast image segmentation based on K-Means clustering with histograms in HSV color space," in 2008 IEEE 10th Workshop on Multimedia Signal Processing, IEEE, pp. 322-325, 2008.
N. Sabri, Z. Ibrahim, and N. N. Rosman, "K-means vs. fuzzy C-means for segmentation of orchid flowers," in 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC), IEEE, pp. 82-86, 2016.
Y. Liu, F. Tang, D. Zhou, Y. Meng, and W. Dong, "Flower classification via convolutional neural network," in 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), IEEE, pp. 110-116, 2016.
M.-M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.-M. Hu, "Global contrast based salient region detection," IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 3, pp. 569-582, 2014.
P. Sathya and R. Kayalvizhi, "Modified bacterial foraging algorithm based multilevel thresholding for image segmentation," Engineering Applications of Artificial Intelligence, vol. 24, no. 4, pp. 595-615, 2011.
K. Bhargavi and S. Jyothi, "A survey on threshold based segmentation technique in image processing," International Journal of Innovative Research and Development, vol. 3, no. 12, pp. 234-239, 2014.
W. Wang, L. Duan, and Y. Wang, "Fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm," Journal of Electrical and Computer Engineering, vol. 2017, 2017.
L. T. Thanh and D. N. Thanh, "An adaptive local thresholding roads segmentation method for satellite aerial images with normalized HSV and lab color models," in Intelligent Computing in Engineering: Springer, pp. 865-872, 2020.
J. Yadav and M. Sharma, "A Review of K-mean Algorithm," Int. J. Eng. Trends Technol, vol. 4, no. 7, pp. 2972-2976, 2013.
I. Garg and B. Kaur, "Color based segmentation using K-mean clustering and watershed segmentation," in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, pp. 3165-3169, 2016.
A. Sharma and S. Sehgal, "Image segmentation using firefly algorithm," in 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect your Worlds, IEEE, pp. 99-102, 2016.
X. Zheng, Q. Lei, R. Yao, Y. Gong, and Q. Yin, "Image segmentation based on adaptive K-means algorithm," EURASIP Journal on Image and Video Processing, vol. 2018, no. 1, pp. 1-10, 2018.
M. R. Hassan, R. R. Ema, and T. Islam, "Color image segmentation using automated K-means clustering with RGB and HSV color spaces," Global Journal of Computer Science and Technology, 2017.
Y. Chen, W. Xu, F. Kuang, and S. Gao, "The Study of Randomized Visual Saliency Detection Algorithm," Computational and mathematical methods in medicine, vol. 2013, 2013.
C. Cooley, S. Coleman, B. Gardiner, and B. Scotney, "Saliency detection and object classification," in Proc. 19th Irish Machine Vision and Image Processing Conf.(IMVIP 2017), pp. 84-90, 2017.
Y. Yangyang and F. Xiang, "A Flower Image Classification Algorithm Based on Saliency Map and PCANet," Journal of Communication and Computer, vol. 15, pp. 14-24, 2019.
M. Deswal and N. Sharma, "A fast HSV image color and texture detection and image conversion algorithm," International Journal of Science and Research (IJSR), vol. 3, no. 6, 2014.
M. Qin, Y. Xi, and F. Jiang, "A new improved convolutional neural network flower image recognition model," in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 3110-3117, 2019.
M. A. H. Abas, N. Ismail, A. I. M. Yassin, and M. N. Taib, "VGG16 for plant image classification with transfer learning and data augmentation," International Journal of Engineering and Technology (UAE), vol. 7, pp. 90-94, 2018.
D. Theckedath and R. Sedamkar, "Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks," SN Computer Science, vol. 1, no. 2, pp. 1-7, 2020.
A. Najjar and E. Zagrouba, "Flower image segmentation based on color analysis and a supervised evaluation," in 2012 International Conference on Communications and Information Technology (ICCIT), IEEE, pp. 397-401 ,2012.
A. H. Ornek and M. Ceylan, "Comparison of traditional transformations for data augmentation in deep learning of medical thermography," in 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), IEEE, pp. 191-194, 2019.
