Original Paper

Machine Vision and Applications

, Volume 19, Issue 2, pp 105-123

First online:

Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects

  • Natalia LariosAffiliated withDepartment of Electrical Engineering, University of Washington Email author 
  • , Hongli DengAffiliated withSchool of Electrical Engineering and Computer Science, Oregon State University
  • , Wei ZhangAffiliated withSchool of Electrical Engineering and Computer Science, Oregon State University
  • , Matt SarpolaAffiliated withDepartment of Mechanical Engineering, Oregon State University
  • , Jenny YuenAffiliated withComputer Science and AI Laboratory, Massachusetts Institute of Technology
  • , Robert PaaschAffiliated withDepartment of Mechanical Engineering, Oregon State University
  • , Andrew MoldenkeAffiliated withDepartment of Botany and Plant Pathology, Oregon State University
  • , David A. LytleAffiliated withDepartment of Zoology, Oregon State University
  • , Salvador Ruiz CorreaAffiliated withDepartment of Diagnostic Imaging and Radiology, Children’s National Medical Center
    • , Eric N. MortensenAffiliated withSchool of Electrical Engineering and Computer Science, Oregon State University
    • , Linda G. ShapiroAffiliated withDepartment of Computer Science and Engineering, University of Washington
    • , Thomas G. DietterichAffiliated withSchool of Electrical Engineering and Computer Science, Oregon State University

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This paper describes a computer vision approach to automated rapid-throughput taxonomic identification of stonefly larvae. The long-term objective of this research is to develop a cost-effective method for environmental monitoring based on automated identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) identification of regions of interest, (b) representation of those regions as SIFT vectors (Lowe, in Int J Comput Vis 60(2):91–110, 2004) (c) classification of the SIFT vectors into learned “features” to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step. We evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria, are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doro-neuria) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.


Classification Object recognition Interest operators Region detectors SIFT descriptor