Cloth Pattern Recognition Using Machine Learning and Neural Network

Visually impaired people face a lot of challenges while choosing clothes with complex patterns and colors. Rotation, scaling and variation in the light makes the cloth recognition problem a challenging task. An automatic cloth pattern recognition technique to classify the patterns into four classes namely plaid, striped, irregular and Patternless is developed using image processing, machine learning and deep learning concepts in this work. MATLAB is used as the simulation tool of choice. Color classification is done with the help of Hue Saturation Intensity (HSI) color model. To recognize clothing patterns, global and local features are extracted. Features extracted include Radon signatures and Grey Level Co-occurrence matrix. Pattern recognition has been done with the help of machine algorithms such as KNN, SVM, and deep learning networks such as AlexNet, GoogleNet, VGG-16 and VGG-19. To evaluate the effectiveness of the algorithms, CCNY Clothing Pattern data-set has been used. The maximum accuracy of 97.9% was obtained using the VGG-19 deep neural network.