Insect Species Recognition using Sparse Representation

Insect species recognition is a typical application of image categorization and object recognition. Unlike generic image categorization datasets (such as the Caltech 101 dataset) that have large variations between categories, the difference of appearance between insect species is so small that only some entomologist experts can distinguish them. Therefore, the state-of-the-art image categorization methods do not perform sufficiently on insect images. In this paper, we propose an insect species recognition method based on class specific sparse representation. On obtaining the vector representation of image via sparse coding of patches, an SVM classifier is used to classify the image into species. We propose two class specific sparse representation methods under weakly supervised learning to discriminate insect species which have substantial similarity to each other. Experimental results show that the proposed methods perform well in insect species recognition and outperform the state-of-the-art methods on generic image categorization.