Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval

This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis

Image Classification: A Survey

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world prob-lems, but then there are various problems in its application processes, such as unsatis-factory effect and extremely low classification accuracy or then and weak adaptive abil-ity. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then com-pletes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network tech-nique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used

Hahmontunnistus merenkulkusovelluksessa syviä neuroverkkoja käyttäen

The aim of this thesis was to study object recognition with the state of the art methods in order to evaluate their potential for the sector of autonomous maritime logistics. In autonomous maritime transportation, object localization and recognition is crucial for safe and efficient traffic flow. In this study, object recognition was studied by training deep convolutional neural networks for image classification and by evaluating their classification and computational performance. In machine learning, a classification algorithm is trained with supervised learning with a dataset of input-output examples. Object recognition is a classification task where objects are classified from images. In deep learning, deep neural networks with multiple layers learn hierarchical representations of the data. For training, they require more computation and data than traditional machine learning methods. In the past years, more and more data has become available, and the computation capacity has increased dramatically. Therefore, deep neural networks have outperformed traditional machine learning algorithms in many tasks, such as object recognition. The best results in object recognition are achieved using deep convolutional neural networks. In the experiments, deep convolutional neural networks were trained for image classification with Rolls-Royce Maritime Image (RRMI) dataset. Small-CNN architecture was generated and trained with random hyperparameter search approach using random weight initialization whereas VGG16, ResNet50 and MobileNet architectures were trained with transfer learning. The classification and computational performances of the models were measured. Transfer learning approach proved to improve classification performance. The VGG16 achieved the best accuracy of 84.0% for the dataset. The best average class accuracy of 78.4% was achieved with the ResNet50. The computational performance of the models was evaluated by measuring the time required for image classification with a CPU and GPU in order to evaluate their potential for a real-time object localization and recognition system. With the GPU, the models were much faster and performed in 3.6-16.0 milliseconds