การวัดความคล้ายกันของภาพด้วยการเชื่อมโยงกราฟแบบลำดับขั้นบนพื้นฐานของอนุกรมวิธาน
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- การค้นคืนภาพดิจิทัลเป็นหัวข้องานวิจัยที่ยังคงท้าทายอย่างมากในสาขาการประมวลผลภาพ มีนักวิจัยหลายกลุ่มพยายามคิดค้นแระยุกต์วิธีการต่างๆเพื่อทำให้ได้ผลลัพธ์ที่ได้จากการค้นคืนดียิ่งขึ้นตรงตามความต้องการมากขึ้น แต่อย่างไรวิธีการเหล่านั้นไม่สามารถที่จะนำมาใช้แทนความหมายของภาพได้อย่างแท้จริง ดังนั้นในงานวิจัยชิ้นนี้ได้นำเสนอแนวทางการวัดความคล้ายกันของภาพด้วยคิวรี่ย้อนกลับบนพื้นฐานการเชื่อมโยงแบบลำดับขั้นบนอนุกรมวิธาน WordNet สำหรับการจัดกลุ่มความหมายภาพ แบ่งขั้นตอนการทำงานออกเป็น 3 ส่วนดังนี้ (1) ขั้นตอนการเตรียมข้อมูล (2) การเชื่อมโยงความสัมพันธ์ข้อมูลภาพ และ (3) การวัดความคล้ายกันของภาพ จากวิธีการที่นำเสนอนี้สามารถวัดความคล้ายกันได้ค่าความถูกต้องสูงถึง 85.7%
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2. Ying Liua, Dengsheng Zhanga, , Guojun Lua, , Wei-Ying Mab,A survey of content-based image retrieval with high-level semantics, Pattern Recognition, Volume 40, Issue 1, January 2007, Pages 262–282.
3. Kevin Lin, Huei-Fang Yang, Kuan-Hsien Liu, Jen-Hao Hsiao, Chu-Song Chen Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search ACM International Conference on Multimedia Retrieval (ICMR), 2015.
4. Jia Li, James Z. Wang. “Automatic linguistic indexing of pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25, 9, pp. 1075-1088.
5. Galleguillos C., Belongie S, Context Based Object Categorization: A Critical Survey. Computer Vision and Image Understanding (CVIU), Vol. 114, 2010, pp. 712-722.
6. Tie Hua Zhou, Ling Wang , and Keun Ho Ryu, Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation , 7,Sustainability 2015, 6303-6320.
7. Russell, B.C., Torralba, A., Murphy, K.P., and Freeman, W.T.. “LabelMe: a database and web-based tool for image annotation,” International Journal Computer Vision, vol.77, 2008.
8. L. Von Ahn, Liu, and M. Blum, “Peekaboom: a game for locating objects in images”, In: Proceedings SIGCHI conference on Human Factors in Computing Systems, 2006, pp. 55–64.
9. K. Barnard, P. Duygulu, D. Forsyth, N. de Freitas, D. M. Blei, and M.I. Jordan, “Matching words and pictures,” J. Mach. Learn. Rese., vol.3, pp. 1107–1135, 2003.
10. M. Johnson and R. Cipolla, “Improved image annotation and labeling through multi-label boosting,” in Brit. Machine Vision Conf., 2005.
11. Venkatesh N. Murthy, Subhransu Maji, R. Manmatha, Automatic Image Annotation using Deep Learning Representations, Proceeding ICMR '15 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015, pp. 603-606.
12. Patwardhan and Pedersen, “Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts,” Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together, pp. 1-8, April 4, 2006, Trento, Italy.
13. Miller, George A. “WordNet: An on-line lexical database,” International Journal of Lexicography, Vol. 3, 1990, pp. 235–312.
14. I. Simon, N. Snavely, and S. Seitz, “Scene summarization for online image collections,” in IEEE Int. Conf. Computer Vision, 2007, pp. 1–8.
15. Y. Wu, J.-Y. Bouguet, A. Nefian, and I. Kozintsev, “Learning concept templates from web images to query personal image databases,”in IEEE Int. Conf. Multimedia and Expo, 2007, pp. 1986–1989.
