Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Automatic supervised information extraction of structured web data

The overall purpose of this project is, in short words, to create a system able to extract vital information from product web pages just like a human would. Information like the name of the product, its description, price tag, company that produces it, and so on. At a first glimpse, this may not seem extraordinary or technically difficult, since web scraping techniques exist from long ago (like the python library Beautiful Soup for instance, an HTML parser1 released in 2004). But let us think for a second on what it actually means being able to extract desired information from any given web source: the way information is displayed can be extremely varied, not only visually, but also semantically. For instance, some hotel booking web pages display at once all prices for the different room types, while medium-sized consumer products in websites like Amazon offer the main product in detail and then more small-sized product recommendations further down the page, being the latter the preferred way of displaying assets by most retail companies. And each with its own styling and search engines. With the above said, the task of mining valuable data from the web now does not sound as easy as it first seemed. Hence the purpose of this project is to shine some light on the Automatic Supervised Information Extraction of Structured Web Data problem. It is important to think if developing such a solution is really valuable at all. Such an endeavour both in time and computing resources should lead to a useful end result, at least on paper, to justify it. The opinion of this author is that it does lead to a potentially valuable result. The targeted extraction of information of publicly available consumer-oriented content at large scale in an accurate, reliable and future proof manner could provide an incredibly useful and large amount of data. This data, if kept updated, could create endless opportunities for Business Intelligence, although exactly which ones is beyond the scope of this work. A simple metaphor explains the potential value of this work: if an oil company were to be told where are all the oil reserves in the planet, it still should need to invest in machinery, workers and time to successfully exploit them, but half of the job would have already been done2. As the reader will see in this work, the way the issue is tackled is by building a somehow complex architecture that ends in an Artificial Neural Network3. A quick overview of such architecture is as follows: first find the URLs that lead to the product pages that contain the desired data that is going to be extracted inside a given site (like URLs that lead to ”action figure” products inside the site ebay.com); second, per each URL passed, extract its HTML and make a screenshot of the page, and store this data in a suitable and scalable fashion; third, label the data that will be fed to the NN4; fourth, prepare the aforementioned data to be input in an NN; fifth, train the NN; and sixth, deploy the NN to make [hopefully accurate] predictions

Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field

A Hybrid Model for Sentiment Analysis Based on Movie Review Datasets

The classification of sentiments, often known as sentiment analysis, is now widely recognized as an open field of research. Over the past few years, a huge amount of study work has been carried out in these disciplines by utilizing a wide variety of research approaches. Due to the possibility that the performance of sentiment analysis may be impacted by the high-dimensional feature set, text mining demands careful consideration during in the construction and selection of features.The process of recognising and extracting subjective information from written data is referred to as sentiment analysis. Sentiment analysis enables companies to understand the social sentiment around their brand, product, or service by monitoring the conversations that take place in internet chat rooms. In order to categorise people's attitudes or sentiments, this study provides a hybrid model (Support Vector Machine, Convolutional Neural Network, and Long Short-Term Memory). The findings of using the network model to sentiment analysis on the movie review or amazon review datasets reveal that it is possible to gain a good classification impact by using the model. The preprocessing is used for text mining, the removal of punctuation, and the generation of vocabulary, also uses GLOVE for vectorization and TF-IDF algorithms for better feature extraction.  The results that were proposed were compared with various base models such as KNN, and MNB, amongst others, which demonstrates that the hybrid model performs better than other models

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.Comment: Accepted to CVPR 2018; Code and data available at https://www.github.com/richzhang/PerceptualSimilarit

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.info:eu-repo/semantics/acceptedVersio