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Hybrid of Statistical and Spectral Texture Features for Vehicle Object Classification System

Affiliations

  • Department of Computer Science, Karpagam University, Coimbatore - 641 021, Tamil Nadu, India
  • Department of Information Technology, Karpagam University, Coimbatore - 641 021, Tamil Nadu, India

Abstract


Objectives: To increase the performance of the classifier for the vehicle object among a mixed and highly texture background using hybrid feature extraction method without pre-processing. Methods/Analysis: Vehicle Object recognition system performance is based on the hybrid of feature vector extraction method and artificial neural network classifier without pre-processing. Every image is divided into single blocks size with 20x20 each. The feature vector is extracted from each single size block of the picture. Normalization is done for the extracted feature vector of the vehicle object in the image. These feature vectors are given as input to the neural network classifier for classification. The feed forward Back Propagation Neural Network (BPNN) algorithm is used to train and test the input feature vector by using Neural Network Classifier (NNC) for the vehicle classification. Findings: The idea of the proposed method is that combining the two different literatures namely statistical and spectral texture features without pre-processing for classification. This method is trained and tested with Illinois at Urbana-Champaign (UIUC) standard databases. UIUC database contains car and non-car images with mixed and highly textured background. The findings indicate that the selected input feature vector improves the classification accuracy rate compared to the previous literature. Also the hybrid features maximize the correct classification and minimize the wrong classification. The improved performance results 90.1% of quantitative evaluation is compared with different literature methods of similar work. Applications/Improvements: In different applications, the proposed method plays vital part in surveillance, security for vehicles, monitoring the traffic, etc.

Keywords

Back Propagation Algorithm, Feature Extraction, Hybrid Feature, Neural Network Classifier, Normalization, Statistical Features, Spectral Texture Features, Vehicle Categorization.

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References


  • Riana JM, Navaneetha KP, Karthikeyan R, Rakesh KS. Multi-layer perception type artificial neural network based traffic control. Indian Journal of Science and Technology. 2016; 9(5):1-6.
  • Singh A, Kumar A, Goudar RH. Online traffic density estimation and vehicle classification management system. Indian Journal of Science and Technology. 2014; 7(4):508– 16.
  • Chandar SM, Sumathi M, Sivanandam SN. Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian Journal of Science and Technology. 2016 Feb; 9(8):1-5.
  • Gharehchopogh FS, Khaze SR, Maleki I. A new approach in bloggers classification with hybrid of K-nearest neighbor and artificial neural network algorithms. Indian Journal of Science and Technology. 2015 Feb; 8(3):237-46.
  • Alex JSR, Mukhedkar ASR, Venkatesan N. Performance analysis of SOFM based reduced complexity feature extraction methods with back propagation neural network for multilingual digit recognition. Indian Journal of Science and Technology. 2015 Aug; 8(18):1-8.
  • Manikandan G, Sairam N, Sharmili S, Venkatakrishnan S. Achieving privacy in data mining using normalization. Indian Journal of Science and Technology. 2013 Apr; 6(4):1-8.
  • Nagarajan B, Balasubramanie P. Object classification in static images with cluttered background using statistical feature based neural classifier. Asian Journal of Information Technology. 2008; 7(4):162-7.
  • Vaid S, Singh P, Kaur C. Classification of human emotions using multi-wavelet transform based features and random forest technique. Indian Journal of Science and Technology. 2015; 8(28):1-7.
  • Nagarajan B, Balasubramanie P. Neural classifier for object classification with cluttered background using spectral texture based features. Journal of Artificial Intelligence. 2008; 1(2):61-9.
  • Setua DK, Awasthi R, Kumar S, Prasad M, Agarwal K. Scanning electron microscopy of natural rubber surfaces: Quantitative statistical and spectral texture analysis using digital image processing. In: Mendez-Vilas A, Diaz J, editors. Microscopy: Science, Technology, Applications and Education. 2010. p. 1642-52.
  • Prakash K, Nagarajan B. Mathematical based approach for object classification. International Journal of Research in Computer Applications and Robotics. 2014; 2(7):58-64.
  • Dhanaseely AJ, Himavathy S, Single SE. Neuron cascaded neural network based face recognition system. Indian Journal of Science and Technology. 2015; 8(27):1-8.
  • Alex JSR, Mukhedkar AS, Venkatesan N. Performance analysis of SOFM based reduced complexity feature extraction methods with back propagation neural network for multi-lingual digit recognition. Indian Journal of Science and Technology. 2015; 8(19):1-8.
  • Jayadurga R, Gunasundari R. Hybrid features based on Zernike moments and Single Value Decomposition (SVD) for vehicle classification system. IJERT. 2016; 5(3):349-53.
  • Agarwal and Roth. UIUC car dataset. 2002. Available from: http://L2r.cs.uiuc.edu/`cogcomp/Data/Car

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