Delineation of Water Bodies from Satellite Images Using MATLAB

This project aims to extract Water Bodies from high resolution satellite images. The hyperspectral images contain data about all the features. The objective is to eliminate everything but the water body pixels present in the imagery. When we are discussing about extraction, quality of the output depends mostly on the input image. Several other factors like, clarity of the image, weather of the area being photographed, clouds present in the atmosphere, the time of day, etc. come into play. Different extraction techniques are used for the purpose. Starting with analyzing the Digital Number values for getting the basic idea of the image, this thesis moves towards Thresholding which is considered to be the first step for classification. Analyzing the images is done by calculating their Radiance and Top of Atmosphere Spectral Reflectance values. Different types of Indices like Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI) with shadow (‘sh’) and non-shadow (‘nsh’) variants are used for the extraction. These indices provide information about the water body pixels and they become the judging criteria for them. As we move on to different images, we put in the method of Adaptive Thresholding to them. This brings in more accuracy as the threshold values are decided according to the images. For further improvement in the extraction, method of Clustering is also applie

Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks

Online media outlets, in a bid to expand their reach and subsequently increase revenue through ad monetisation, have begun adopting clickbait techniques to lure readers to click on articles. The article fails to fulfill the promise made by the headline. Traditional methods for clickbait detection have relied heavily on feature engineering which, in turn, is dependent on the dataset it is built for. The application of neural networks for this task has only been explored partially. We propose a novel approach considering all information found in a social media post. We train a bidirectional LSTM with an attention mechanism to learn the extent to which a word contributes to the post's clickbait score in a differential manner. We also employ a Siamese net to capture the similarity between source and target information. Information gleaned from images has not been considered in previous approaches. We learn image embeddings from large amounts of data using Convolutional Neural Networks to add another layer of complexity to our model. Finally, we concatenate the outputs from the three separate components, serving it as input to a fully connected layer. We conduct experiments over a test corpus of 19538 social media posts, attaining an F1 score of 65.37% on the dataset bettering the previous state-of-the-art, as well as other proposed approaches, feature engineering or otherwise.Comment: Accepted at SIGIR 2018 as Short Pape

HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language Worksho

HoME: a Household Multimodal Environment

International audienceWe introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting

HoME: a Household Multimodal Environment

International audienceWe introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting