Multilinear feature extraction and classification of multi-focal images, with applications in nematode taxonomy
In this paper, we present a 3D X-Ray Transform based multilinear feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological information for a transparent specimen can be captured in the form of a stack of high-quality images, representing individual focal planes through the specimen's body. We present a method that can effectively exploit the entire information in the stack using the 3D X-Ray projections at different viewing angles. These DMI stacks represent the effect of different factors - shape, texture, viewpoint, different instances within the same class and different classes of specimens. For this purpose, we embed the 3D X-Ray Transform within a multilinear framework and propose a Multilinear X-Ray Transform (MXRT) feature representation. By combining the tensor texture and shape information we can get better recognition rates than just relying on the original or key frames of DMI stacks. The experimental results on the nematode DMI data show that the 3D X-Ray Transform based multilinear analysis method can effectively give 100% recognition rate on a real-life database.