18,803 research outputs found
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Automated annotation of landmark images using community contributed datasets and web resources
A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image
analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of
0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases
Image-based Text Classification using 2D Convolutional Neural Networks
We propose a new approach to text classification
in which we consider the input text as an image and apply
2D Convolutional Neural Networks to learn the local and
global semantics of the sentences from the variations of the
visual patterns of words. Our approach demonstrates that
it is possible to get semantically meaningful features from
images with text without using optical character recognition
and sequential processing pipelines, techniques that traditional
natural language processing algorithms require. To validate
our approach, we present results for two applications: text
classification and dialog modeling. Using a 2D Convolutional
Neural Network, we were able to outperform the state-ofart
accuracy results for a Chinese text classification task and
achieved promising results for seven English text classification
tasks. Furthermore, our approach outperformed the memory
networks without match types when using out of vocabulary
entities from Task 4 of the bAbI dialog dataset
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
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