Invariant Image-Based Species Classification of Butterflies and Reef Fish

By Hafeez Anwar, Sebastian Zambanini, Martin Kampel. 2015

3 citations recorded by the source corpus.

We propose a framework for species-based image classification of butterflies and reef fish. To support such image-based classification, we use an image representation which enriches the famous bag-of-visual words (BoVWs) model with spatial information. This image representation is developed by encoding the global geometric relationships of visual words in the 2D image plane in a scaleand rotation-invariant manner. In this way, invariance is achieved to the most common variations found in the images of these animals as they can be imaged at different image locations, exhibit various in-plane orientations and have various scales in the images. The images in our butterfly and reef fish datasets belong to 30 species of each animal. We achieve better classification rates on both the datasets than the ordinary BoVWs model while still being invariant to the mentioned image variations. Our proposed image-based classification framework for butterfly and reef fish species can be considered as a helpful tool for scientific research, conversation and education.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Plant Science

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Citations in the corpus

NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation (2020, background): “Of the datasets which do allow for species identification currently, most primarily focus on the development of landbased camera trap systems (2, 20), although work has also been undertaken in the development of marine life species detection systems (1).”