Content-Based Image Retrieval (CBIR)

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SynthText

Visual Geometry Group - University of Oxford

Computer Vision group from the University of Oxford

www.robots.ox.ac.uk

In this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene geometry. Second, we use the synthetic images to train a Fully-Convolutional Regression Network (FCRN) which efficiently performs text detection and bounding-box regression at all locations and multiple scales in an image. We discuss the relation of FCRN to the recently-introduced YOLO detector, as well as other end-to-end object detection systems based on deep learning. The resulting detection network significantly out performs current methods for text detection in natural images, achieving an F-measure of 84.2% on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per second on a GPU.

GitHub - ankush-me/SynthText: Code for generating synthetic text images as described in "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016.

Code for generating synthetic text images as described in "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016. - GitHub - a...

github.com

Visual Geometry Group - University of Oxford

Computer Vision group from the University of Oxford

www.robots.ox.ac.uk

BBC News Text Search

zeus.robots.ox.ac.uk

 

 

Statistical Association Methods for Mechanized Documentation

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VGG Image Classification (VIC) Engine​

Visual Geometry Group - University of Oxford

Computer Vision group from the University of Oxford

www.robots.ox.ac.uk

Visual Geometry Group - University of Oxford

Computer Vision group from the University of Oxford

www.robots.ox.ac.uk

Love affair with CBIR Part 4

Love affair with CBIR (Content base image retrieval system) Part 4 Update:I never figure out, this would be series of article, here are links to rest of it Love affair with CBIR – Part 1 Love…

sbrakl.wordpress.com

 

https://dl.acm.org/doi/10.1145/3308558.3314142

 

GitHub - khalooei/ALOCC-CVPR2018: Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC)

Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC) - GitHub - khalooei/ALOCC-CVPR2018: Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC)

github.com

[CVPR Poster] [presentation] [Project] [Paper] [tensorflow code] [keras code]


This work was inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised and semi-supervised settings. We proposed an end-to-end architecture for one-class classification. The architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples. One network works as the novelty detector, while the other supports it by enhancing the inlier samples and distorting the outliers. The intuition is that the separability of the enhanced inliers and distorted outliers is much better than deciding on the original samples.


Here is the preliminary version of the code on grayscale databases.

 

An Extensive Dataset of UML Models in GitHub

The Unified Modeling Language (UML) is widely taught in academia and has good acceptance in industry. However, there is not an ample dataset of UML diagrams publicly available. Our aim is to offer a dataset of UML files, together with meta-data of the software projects where the UML files belong...

ieeexplore.ieee.org

This approach can be used to extract images as well (jpg, png, etc.).

Referenced in the article:

GitHub - chaoss/grimoirelab-perceval: Send Sir Perceval on a quest to retrieve and gather data from software repositories.

Send Sir Perceval on a quest to retrieve and gather data from software repositories. - GitHub - chaoss/grimoirelab-perceval: Send Sir Perceval on a quest to retrieve and gather data from software r...

github.com

 

Source: AssisTag: Seamless Integration of Content-based and Keyword-based Image Exploration for Category Search

 

Interactive visualization and analysis of multimodal datasets for surgical applications - PubMed

Surgeons use information from multiple sources when making surgical decisions. These include volumetric datasets (such as CT, PET, MRI, and their variants), 2D datasets (such as endoscopic videos), and vector-valued datasets (such as computer simulations). Presenting all the information to the...

pubmed.ncbi.nlm.nih.gov

 

Look, Read and Enrich - Learning from Scientific Figures and their Captions​

Compared to natural images, understanding scientific figures is particularly hard for machines. However, there is a valuable source of information in scientific literature that until now has remained untapped: the correspondence between a figure and its caption. In this paper we investigate what can be learnt by looking at a large number of figures and reading their captions, and introduce a figure-caption correspondence learning task that makes use of our observations. Training visual and language networks without supervision other than pairs of unconstrained figures and captions is shown to successfully solve this task. We also show that transferring lexical and semantic knowledge from a knowledge graph significantly enriches the resulting features. Finally, we demonstrate the positive impact of such features in other tasks involving scientific text and figures, like multi-modal classification and machine comprehension for question answering, outperforming supervised baselines and ad-hoc approaches.

Code: https://github.com/HybridNLP2018/LVC