Object Recognition using Self Learned Features
A great deal of research has been centered around developing algorithms forlearning features from unlabelled information. Much advance has been made onbenchmark datasets by utilizing progressively complex unsupervised learning algorithmsand deep models. However, the time required to train such deep networksis a major drawback. This thesis presents a generalized trainable frameworkfor object detection in static images. In this work, we have used a ConvolutionalNeural Network (CNN) for training and obtained good classificationresults in terms of accuracy. The main idea is to learn features from the data itself(in unsupervised way) and then apply a classifier (in supervised way) to classify.We have used CNN to extract useful hierarchical features using natural images as training images. The learned convolutional kernels (weights) are appliedonto MNIST and CIFAR-10 datasets to extract their features. We then use CNNnetwork for classification. Despite the simplicity of our network, we achieve accuracyas good as previously published results on MNIST and CIFAR-10 datasets.Keywords: Object recognition, deep learning, Deep Neural Network (DNN), ConvolutionalNeural Network (CNN).
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