Pose estimation-dependent identification method for field moth images using deep learning architecture

Due to the varieties of moth poses and cluttered background, traditional methods for automated identification of on-trap moths suffer problems of incomplete feature extraction and misidentification. A novel pose estimation-dependent automated identification method using deep learning architecture is proposed in this paper for on-trap field moth sample images. To deal with cluttered background and uneven illumination, two-level automated moth segmentation was created for separating moth sample images from each trap image. Moth pose was then estimated in terms of either top view or side view. Suitable combinations of texture, colour, shape and local features were extracted for further moth description. Finally, the improved pyramidal stacked de-noising auto-encoder (IpSDAE) architecture was proposed to build a deep neural network for moth identification. The experimental results on 762 field moth samples by 10-fold cross-validation achieved a good identification accuracy of 96.9%, and indicated that the deployment of the proposed pose estimation process is effective for automated moth identification.

[1]  Sang-Hee Lee,et al.  Butterfly species identification by branch length similarity entropy , 2012 .

[2]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[3]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[4]  Valda Rondelli,et al.  Automatic trap for moth detection in integrated pest management , 2011 .

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[7]  Kevin J. Gaston,et al.  Automating insect identification: exploring the limitations of a prototype system , 1999 .

[8]  Jun Lv,et al.  An Insect Imaging System to Automate Rice Light-Trap Pest Identification , 2012 .

[9]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[10]  Luca Maria Gambardella,et al.  High-Performance Neural Networks for Visual Object Classification , 2011, ArXiv.

[11]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[12]  Qingyuan Zhu,et al.  Dimension reduction analysis in image-based species classification , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[13]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[14]  Huosheng Hu,et al.  A pyramidal deep learning architecture for human action recognition , 2014, Int. J. Model. Identif. Control..

[15]  Norman I. Platnick,et al.  9 Introducing SPIDA-Web: Wavelets, Neural Networks and Internet Accessibility in an Image-Based Automated Identification System , 2007 .

[16]  WangJiangning,et al.  A new automatic identification system of insect images at the order level , 2012 .

[17]  Stefan Schröder,et al.  Biodiversity Informatics in Action: Identification and Monitoring of Bee Species using ABIS , 2001 .

[18]  M. O'Neill,et al.  Automated species identification: why not? , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[19]  Chenglu Wen,et al.  Local feature-based identification and classification for orchard insects , 2009 .

[20]  Javier Silvestre-Blanes.  Structural similarity image quality reliability: Determining parameters and window size , 2011, Signal Process..

[21]  Chenglu Wen,et al.  Image-based orchard insect automated identification and classification method , 2012 .

[22]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[23]  Y-Lan Boureau,et al.  Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.

[24]  Liqiang Ji,et al.  The identification of butterfly families using content-based image retrieval , 2012 .

[25]  Jiangning Wang,et al.  A new automatic identification system of insect images at the order level , 2012, Knowl. Based Syst..

[26]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[27]  Lakhmi C. Jain,et al.  An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant , 2011, Applied Intelligence.