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Identification and Retrieval of Moth Images Based on Wing Patterns

  • Linan FengEmail author
  • Bir Bhanu
  • John Heraty
Chapter
Part of the Computational Biology book series (COBO, volume 22)

Abstract

Moths are important life forms on the planet with approximately 160,000 species discovered. Entomologists in the past need to manually collect moth samples, take digital photos, identify the species, and archive into different categories. This process is time-consuming and requires a lot of human labors. As modern technologies in computer vision and machine learning advance, new algorithms have been developed in recognizing objects in digital images based on their visual attributes. The methods can also be applied to the entomology domain for recognizing biological identities. The Lepidoptera (moths and butterflies) in general can be identified and classified by their body morphological features; wing visual patterns that can be obtained using various image processing approaches in automated diagnostic systems. In this chapter, we describe a system for automated moth species identification and retrieval. The core of the system is a probabilistic model that infers semantically related visual (SRV) attributes from low-level visual features of the moth images in the training set, where moth wings are segmented into information-rich patches from which the local features are extracted, and the SRV attributes are provided by human experts as ground-truth. For the testing images in the database, an automated identification process is evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further propose a novel network analysis-based approach to explore and utilize the co-occurrence patterns of SRV attributes as contextual cues to improve individual attribute detection accuracy. The effectiveness of the proposed approach is evaluated in automated moth identification and attribute-based image retrieval. In addition, a novel image descriptor called SRV attribute signature is introduced to record the visual and semantic properties of an image and is used to compare image similarity. Experiments are performed on an existing entomology database to illustrate the capabilities of our proposed system.

Keywords

Image Retrieval Relevance Feedback Scale Invariant Feature Transform Mean Average Precision Salient Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was supported in part by the National Science Foundation grants 0641076 and 0905671.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Research in Intelligent Systems, Bourns College of EngineeringUniversity of California at RiversideRiversideUSA
  2. 2.Entomology DepartmentUniversity of California at RiversideRiversideUSA

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