Analysis of Multiple Features and Classifier Techniques Combination for Image Pattern Recognition

  • Ashish ShindeEmail author
  • Abhijit Shinde
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 673)


Automatic visual pattern recognition is complex and highly researched area of image processing. This research aims to study various pattern recognition algorithms, cloth pattern recognition is presented as research problem and to find out best combination suited for the cloth pattern recognition problem. The dataset is collected from CCNY clothing pattern dataset and contains 150 samples of each category (Patternless, Striped, Plaid, and Irregular). The presented study compares all combinations of three different feature extraction techniques and three classifier techniques. Feature extraction techniques used here are Radon Feature Extraction, projection of rotated gradient, and quantized histogram of gradients. The classifiers used are KNN, neural network, and SVM classifier. The highest recognition rate is achieved using Radon Signature feature and KNN classifier combination which reaches to 93.7% of accuracy.


Clothing pattern recognition Texture analysis Image feature extraction Classifier 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Sinhgad College of EngineeringPuneIndia
  2. 2.Bhima Institute of Management and TechnologyKolhapurIndia

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