In this paper, we propose to use projection methods such as coefficient of variation projection (COV) to exploit the entire information of Digital Multi-focal Images (DMI) using its projection images along different directions. The COV projection takes into account the intensity distribution feature of multi-focal images, so it overcomes the limitation of poor contrast of the projection images from the 3D X-Ray Transform, which is used in a previous work. Because the DMI stacks represent the effect of different factors - texture, projection directions, different instances within the same class and different classes of objects, we embed the projection method within a multilinear classification framework. The experimental results on the nematode data show that the image projection based multilinear classifier can achieve very reliable recognition rate (95.5%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work.
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