An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant

The main objective of this paper is to investigate the use of Quality Threshold ARTMAP (QTAM) neural network in classifying the feature vectors generated by moment invariant for the insect recognition task. In this work, six different types of moment invariant technique are adopted to extract the shape features of the insect images. These moment techniques are Geometrical Moment Invariant (GMI), United Moment Invariant (UMI), Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI), Tchebichef Moment Invariant (TMI) and Krawtchouk Moment Invariant (KMI). All the moment techniques are analyzed using the concept of intraclass and interclass analysis. In intraclass analysis, several computation methods are introduced in order to examine the invariance properties of adopted moment techniques for the same insect object. Meanwhile, the classification accuracy of neural networks is adopted to measure the interclass characteristic and the effectiveness of moment technique in extracting the shape features of insect images. Other types of neural networks are also utilized in this research work. This includes novel enhancement technique based on the Gaussian and Mahalanobis function that design to increase its prediction accuracy. All the other networks used to classify the feature vectors are based on the Fuzzy ARTMAP (FAM) neural network. The experimental results indicated that the Krawtchouk Moment Invariant technique generated the highest classification accuracy for most of the networks used and generated the smallest error for the intraclass analysis. Using different normalization technique, the Quality Threshold ARTMAP and Mahalanobis distance function (QTAM-m) network gave the highest insect recognition results when compared to other networks.

[1]  Scott E. Umbaugh,et al.  Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom , 1997 .

[2]  Raveendran Paramesran,et al.  Classification of rice grains using fuzzy artmap neural network , 2002, Asia-Pacific Conference on Circuits and Systems.

[3]  Boaz Lerner,et al.  The Bayesian ARTMAP , 2007, IEEE Transactions on Neural Networks.

[4]  Sim Heng Ong,et al.  Image Analysis by Tchebichef Moments , 2001, IEEE Trans. Image Process..

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

[6]  Qing Chen,et al.  Optical character recognition for model-based object recognition applications , 2003, The 2nd IEEE Internatioal Workshop on Haptic, Audio and Visual Environments and Their Applications, 2003. HAVE 2003. Proceedings..

[7]  Reinhard Klette,et al.  Footprint Recognition of Rodents and Insects , 2004 .

[8]  Ian Witten,et al.  Data Mining , 2000 .

[9]  A. Saradha,et al.  A Hybrid Feature Extraction Approach for Face Recognition Systems , 2005 .

[10]  Miroslaw Pawlak,et al.  On Image Analysis by Moments , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  刘伟军,et al.  United moment invariants for shape discrimination , 2003 .

[12]  Huazhong Shu,et al.  Blurred Image Recognition by Legendre Moment Invariants , 2010, IEEE Transactions on Image Processing.

[13]  Jen-Reng.  Object Detection using Geometric Invariant Moment , 2006 .

[14]  Karim Faez,et al.  Unsupervised classification of handwritten Farsi numerals using evolution strategies , 1997, TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162).

[15]  T. N. Janakiraman,et al.  Automated Motion Tracking of Insects Using Invariant Moments in Image Sequence , 2007 .

[16]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[18]  Mark A. O'Neill,et al.  Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY) , 2004 .

[19]  Zhen Ji,et al.  Local affine transform invariant image watermarking by Krawtchouk moment invariants , 2007, IET Inf. Secur..

[20]  James R. Williamson,et al.  Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps , 1996, Neural Networks.

[21]  Ángel García-Crespo,et al.  Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches , 2011, Applied Intelligence.

[22]  M. Dehghan,et al.  Farsi handwritten character recognition with moment invariants , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[23]  Thomas G. Dietterich,et al.  Automated Insect Identification through Concatenated Histograms of Local Appearance Features , 2007, WACV.

[24]  Min Shi,et al.  Support vector machines for traffic signs recognition , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[25]  Kevin J. Gaston,et al.  Species-identification of wasps using principal component associative memories , 1999, Image Vis. Comput..

[26]  R. Paramesran,et al.  Derivation of blur-invariant features using orthogonal Legendre moments , 2007 .

[27]  H. Qjidaa,et al.  Krawtchouk moment feature extraction for neural Arabic handwritten words recognition , 2009, 2009 International Conference on Multimedia Computing and Systems.

[28]  Hongzhi Song,et al.  Identification Algorithm of Winged Insects Based on Hybrid Moment Invariants , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[29]  G. A. Vijayalakshmi Pai,et al.  Image Recognition Using Simplified Fuzzy Artmap Augmented with a Moment Based Feature Extractor , 2000, Int. J. Pattern Recognit. Artif. Intell..

[30]  André Quinquis,et al.  Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment , 2006, EURASIP J. Adv. Signal Process..

[31]  B. Potocnik,et al.  Assessment of Region-Based Moment Invariants for Object Recognition , 2006, Proceedings ELMAR 2006.

[32]  R. Sukanesh,et al.  A Novel Feature Extraction Technique for Face Recognition , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[33]  Tao Xiong,et al.  A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[34]  S. Grossberg,et al.  A self-organizing neural network for supervised learning, recognition, and prediction , 1992, IEEE Communications Magazine.

[35]  Chee-Way Chong,et al.  An approach to 3-D object recognition using Legendre moment invariants , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[36]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[37]  H. Fleyeh,et al.  Invariant Road Sign Recognition with Fuzzy ARTMAP and Zernike Moments , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[38]  Cecilia Di Ruberto,et al.  Moment-Based Techniques for Image Retrieval , 2008, 2008 19th International Workshop on Database and Expert Systems Applications.

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

[40]  Raveendran Paramesran,et al.  Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network , 2000, Inf. Sci..

[41]  Fabrice Heitz,et al.  Affine-invariant geometric shape priors for region-based active contours , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Shijie Dai,et al.  Vehicle-logo Recognition Method Based on Tchebichef Moment Invariants and SVM , 2009, 2009 WRI World Congress on Software Engineering.

[43]  Michael Mayo,et al.  Automatic species identification of live moths , 2006, Knowl. Based Syst..

[44]  Chee Peng Lim,et al.  A novel Euclidean quality threshold ARTMAP network and its application to pattern classification , 2010, Neural Computing and Applications.

[45]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[46]  Mingyin Yao,et al.  Study on Image Recognition of Insect Pest of Sugarcane Cotton Aphis Based on Rough Set and Fuzzy C-means Clustering , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[47]  Le Song,et al.  Study on the Automatic Instrumental Reading System Based on Image Processing , 2007, 2007 International Conference on Mechatronics and Automation.

[48]  Karim Faez,et al.  Neural network based face recognition with moment invariants , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[49]  Barbara Webb,et al.  Entropy-based visual homing , 2009, 2009 International Conference on Mechatronics and Automation.

[50]  Razvan Andonie,et al.  Fuzzy ARTMAP with input relevances , 2006, IEEE Transactions on Neural Networks.

[51]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..

[52]  A. Venkataramana,et al.  Radial Krawtchouk moments for rotational invariant pattern recognition , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[53]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[54]  Martin Drauschke,et al.  Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures , 2008, Apidologie.

[55]  Baopu Li,et al.  Computer aided detection of bleeding in capsule endoscopy images , 2008, 2008 Canadian Conference on Electrical and Computer Engineering.

[56]  S. Rajasekaran,et al.  Image recognition using analog-ART1 architecture augmented with moment-based feature extractor , 2004, Neurocomputing.

[57]  N. Macleod,et al.  Automated Taxon Identification in Systematics : Theory, Approaches and Applications , 2007 .

[58]  Ethem Alpaydin,et al.  Constructive Feedforward ART Clustering Networks — Part I , 2001 .

[59]  Ethem Alpaydin,et al.  Constructive feedforward ART clustering networks. I , 2002, IEEE Trans. Neural Networks.

[60]  Puteh Saad,et al.  GENERALIZATION PERFORMANCE ANALYSIS BETWEEN FUZZY ARTMAP AND GAUSSIAN ARTMAP NEURAL NETWORK , 2007 .

[61]  Rang-ding Wang,et al.  A Video Zero-Watermark Algorithm against RST Attacks , 2009, 2009 Asia-Pacific Conference on Information Processing.

[62]  Venkataramana Appala,et al.  Image Watermarking Using Krawtchouk Moments , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[63]  Keith C. Norris,et al.  A test of a pattern recognition system for identification of spiders , 1999 .

[64]  Mei Fangquan,et al.  Measuring geometrical features of insect specimens using image analysis. , 2002 .

[65]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[66]  Mark A. O'Neill,et al.  DAISY: A Practical Tool for Semi-Automated Species Identification , 2006 .

[67]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[68]  Stephen Grossberg,et al.  Fuzzy ARTMAP: an adaptive resonance architecture for incremental learning of analog maps , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.