Butterfly image retrieval based on SIFT feature analysis
Huan Hao, Cheng Cai, Yu Meng, Wei Guo Song, Xiang Qin, Huiyan Zhao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.