{'fraction_non_alphanumeric': 0.015236567762630313, 'fraction_numerical': 0.00400962309542903, 'mean_word_length': 5.058252427184466, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Using Deep Convolutional Neural Network in Computer Vision for Real-World Scene Classification

Scene is a view which contains various objects in a real-world environment. The global view of an image can be called as scene classification. Scene classification is a very challenging work to be done by computers as it is very difficult for a computer to recognize the global view of an image. Therefore, this task is one of the challenging tasks in computer vision area. Object classification task has drastically improved by using the Deep Learning, Alex Net Convolutional Neural Network. Highly motivated from this work, we used one of the already trained architecture of deep learning called Alex Net Convolutional Neural Network for extracting the features of input image automatically and then applying the transfer learning approach for classification task to reduce the overall computational complexity of the neural network. We then have performed scene classification task on various classifiers and then computer their accuracies which comes out to be greater than the state-of-the-art methods. To perform scene classification task, the dataset we used is the Places dataset containing 2.5 million real-world images and 201 scene classes.