Translator Disclaimer
10 July 2009 Butterfly image retrieval based on SIFT feature analysis
Author Affiliations +
Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 74890O (2009)
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China
Butterfly image retrieval is very important in the insect recognition research area but the existing butterfly retrieval technology presents poor performance. SIFT (Scale Invariant Feature Transform) features are reliable because they are insensitive to image scale, rotation, affine, distortion and change in illumination. The local and multiscale natures of the SIFT feature make it create better performance than other existing approaches do. In this paper, a new butterfly image retrieval algorithm based on SIFT feature is presented. The butterfly images in this research are transformed into a set of SIFT feature descriptors, and then the similarity of feature points is described by using Euclidean distance. Experimental results demonstrate that the method based on SIFT feature provides a new effective way for butterfly image retrieval. This proposed algorithm is invariant to the changes of butterfly image scale, rotation, and transformation. It is also robust to distortion and occlusion. Compared with the method of using gray histogram, the performance of butterfly image retrieval based on SIFT feature is improved significantly.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Huan Hao, Cheng Cai, Yu Meng, Wei Song, Xiang Qin, and Huiyan Zhao "Butterfly image retrieval based on SIFT feature analysis", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890O (10 July 2009);


No training blind image quality assessment
Proceedings of SPIE (March 07 2014)
Framework for image mining and retrieval
Proceedings of SPIE (June 23 2003)
Gradient theoretic clustering: a theoretic model
Proceedings of SPIE (July 15 2004)
On latent fingerprint enhancement
Proceedings of SPIE (April 14 2010)

Back to Top