Deep residual learning for image recognition. Free-hand Sketch Recognition Classification. This is because the kernels are able to learn different feature representations which help the model to differentiate between the classes well. CNN gives the best performance with a of 96.01%.XGBoost shows best performance as compared to all the other non-deep learning models as the dataset includes images of multiple classes over which XGboost is able to learn better because of boosting technique.Texture based features gave good classification accuracy as compared to other features.In Dimensionality reduction technique LDA performs better than PCA as it is able to separate data on the basis of classes.It can be extended by replacing the doodles with doodles of alphabets and then convert the hand-written text into digital text format.This application can be used as a fast prototyping tool for designers or artists by suggesting them the accurate templates on the basis of the rough doodles made by them.The Quick Draw Doodle Recognition challenge is a good example of these issues because different users may draw the same object differently or the doodles could be incomplete which is similar to noisy data. It is a challenge in Computer Vision & Machine Learning to handle noisy data and dataset with many different representations of the same class.

Project Poster can be found in CV-Poster-Final.pdf. By this project we are trying to achieve the same using different feature extraction techniques like HOG, LBP, SIFT, SURF, pixel values with feature reduction techniques PCA, LDA and applying various classifiers such as Naive Bayes, Random Forest, SVM, XGBoost, Bagging, ADA-boost, KNN and CNN to compare their performance on different evaluation metric such as Accuracy, CMC Curve and Confusion Matrix. In Quick Draw the AI system tries to classify the hand-drawn doodle into a predetermined category.

This project is done as a part of Computer Vision Course.

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