Machine Vision and Applications

, Volume 19, Issue 2, pp 105–123 | Cite as

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

  • Natalia Larios
  • Hongli Deng
  • Wei Zhang
  • Matt Sarpola
  • Jenny Yuen
  • Robert Paasch
  • Andrew Moldenke
  • David A. Lytle
  • Salvador Ruiz Correa
  • Eric N. Mortensen
  • Linda G. Shapiro
  • Thomas G. Dietterich
Original Paper

Abstract

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.

Keywords

Classification Object recognition Interest operators Region detectors SIFT descriptor 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Natalia Larios
    • 1
  • Hongli Deng
    • 2
  • Wei Zhang
    • 2
  • Matt Sarpola
    • 3
  • Jenny Yuen
    • 6
  • Robert Paasch
    • 3
  • Andrew Moldenke
    • 4
  • David A. Lytle
    • 5
  • Salvador Ruiz Correa
    • 7
  • Eric N. Mortensen
    • 2
  • Linda G. Shapiro
    • 8
  • Thomas G. Dietterich
    • 2
  1. 1.Department of Electrical EngineeringUniversity of WashingtonSeattleUSA
  2. 2.School of Electrical Engineering and Computer ScienceOregon State UniversityCorvallisUSA
  3. 3.Department of Mechanical EngineeringOregon State UniversityCorvallisUSA
  4. 4.Department of Botany and Plant PathologyOregon State UniversityCorvallisUSA
  5. 5.Department of ZoologyOregon State UniversityCorvallisUSA
  6. 6.Computer Science and AI LaboratoryMassachusetts Institute of TechnologyCambridgeUSA
  7. 7.Department of Diagnostic Imaging and RadiologyChildren’s National Medical CenterWashingtonUSA
  8. 8.Department of Computer Science and EngineeringUniversity of WashingtonSeattleUSA

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