Publication Date:
2013-09-05
Description:
Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge forthe advancement of this field. Here we present an automated species identification method for wildlife pictures captured by remote camera traps. Our process starts with images that are cropped out of the background. We then use improved sparse coding spatial pyramid matching (ScSPM), which extracts dense SIFT descriptor and cell structured LBP (cLBP) as the local features, generates global feature via weighted sparse coding and max pooling using multi-scale pyramid kernel, and classifies the images by a linear SVM algorithm. Weighted sparse coding is used to enforce both sparsity and locality of encoding in feature space. We test the method on adataset with over 7000 camera trap images of 18 species from two different field cites, and achieved an average classification accuracy of 82%. Our analysis demonstrates that the combination of SIFT and cLBP can serve as a quite useful technique for animal species recognition in real, complex scenarios.
Print ISSN:
1687-5281
Electronic ISSN:
1687-5176
Topics:
Electrical Engineering, Measurement and Control Technology
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