Automatic Animal Species Identification Based on Camera Trapping Data

  • Author / Creator
    Wang, Baoliang
  • The classification of animal images based on camera trapping data is an important and challenging task in the domains of computer vision, machine learning and ecological management. This thesis presents an animal species identification system that can automatically identify the species of an animal captured in an image by a camera trap. We use the Fisher Vector coding approach on top of the dense Scale Invariant Feature Transform features and the cell-structured Local Binary Pattern descriptors to generate a fixed length vector representation for each image and then feed this vector representation to the linear Support Vector Machines for learning and classification. Unlike traditional Bag of Visual Words models that only use a generative method or discriminative method, the powerful Fisher Kernel framework combines the advantages of both generative and discriminative approaches to encode image descriptors and then classify images. The key idea is to characterize an image with a gradient vector derived from a generative probability model and to subsequently feed this gradient vector to a discriminative classifier. Instead of only using zero-order image statistics like in conventional approaches, the Fisher Vector coding method retains zero-order, first-order and second-order information and thus allows less image approximation error. Extensive experimental study shows that our method achieves the highest classification accuracy compared to various conventional.

  • Subjects / Keywords
  • Graduation date
    Fall 2014
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.