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.
A derivative of Fisher's Linear Discriminant Analysis (FLDA), named Fisherapples for the recognition of apple lesions
which is not sensitive to large variations in illumination is proposed in this paper. We make use of the linear projection
that is orthogonal to the within-class scatter of the apple images from a high-dimensional image space to a considerably
low-dimensional image space. It separates the data-cases well, projecting away variations in lighting. Our approach
maximizes the ratio of between-class scatter to that of within-class scatter of apple lesions, i.e., we can get maximal
between-class distances and minimal within-class distances after projection. This implies that the gap between the classes
becomes bigger and ensures optimal separability in the new space. Besides, we take advantage of Principal Component
Analysis (PCA) to project the set of apple images to a lower dimensional space in order to overcome the complication of
the singular within-class scatter matrix. After that, the resulting within-class scatter becomes nonsingular and
subsequently we can use standard FLDA to reduce the dimension further. Consequently, it is effortless for the computer
to calculate the result. Experimental results demonstrate that Fisherapples performs better in apple lesion recognition
than PCA.
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